diff --git a/README.md b/README.md index f44e2201a5..8262ae4447 100644 --- a/README.md +++ b/README.md @@ -116,7 +116,7 @@ FiftyOne. ## Contributing to FiftyOne -FiftyOne and [FiftyOne-Brain](https://github.com/voxel51/fiftyone-brain) are +FiftyOne and [FiftyOne Brain](https://github.com/voxel51/fiftyone-brain) are open source and community contributions are welcome! Check out the diff --git a/docs/source/user_guide/brain.rst b/docs/source/brain.rst similarity index 99% rename from docs/source/user_guide/brain.rst rename to docs/source/brain.rst index 0eacb93e1b..6a569c8f31 100644 --- a/docs/source/user_guide/brain.rst +++ b/docs/source/brain.rst @@ -5,9 +5,9 @@ FiftyOne Brain .. default-role:: code -The FiftyOne Brain provides powerful machine learning techniques that are -designed to transform how you curate your data from an art into a measurable -science. +The `FiftyOne Brain ` provides +powerful machine learning techniques that are designed to transform how you +curate your data from an art into a measurable science. .. note:: diff --git a/docs/source/user_guide/dataset_zoo/api.rst b/docs/source/dataset_zoo/api.rst similarity index 100% rename from docs/source/user_guide/dataset_zoo/api.rst rename to docs/source/dataset_zoo/api.rst diff --git a/docs/source/user_guide/dataset_zoo/datasets.rst b/docs/source/dataset_zoo/datasets.rst similarity index 100% rename from docs/source/user_guide/dataset_zoo/datasets.rst rename to docs/source/dataset_zoo/datasets.rst diff --git a/docs/source/user_guide/dataset_zoo/index.rst b/docs/source/dataset_zoo/index.rst similarity index 100% rename from docs/source/user_guide/dataset_zoo/index.rst rename to docs/source/dataset_zoo/index.rst diff --git a/docs/source/user_guide/dataset_zoo/remote.rst b/docs/source/dataset_zoo/remote.rst similarity index 100% rename from docs/source/user_guide/dataset_zoo/remote.rst rename to docs/source/dataset_zoo/remote.rst diff --git a/docs/source/index.rst b/docs/source/index.rst index afed9fa3b7..d60c18bba9 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -351,7 +351,7 @@ models. .. custombutton:: :button_text: Learn more about the Brain - :button_link: user_guide/brain.html + :button_link: brain.html .. code-block:: python :linenos: @@ -380,6 +380,71 @@ that execute on a connected workflow orchestration tool like Apache Airflow. :alt: fiftyone-plugins :align: center +Dataset Zoo +----------- + +The FiftyOne Dataset Zoo provides a powerful interface for downloading datasets +and loading them into FiftyOne. + +It provides native access to dozens of popular benchmark datasets, and it als +supports downloading arbitrary public or private datasets whose +download/preparation methods are provided via GitHub repositories or URLs. + +.. custombutton:: + :button_text: Check out the Dataset Zoo + :button_link: dataset_zoo/index.html + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset("coco-2017", split="validation") + + session = fo.launch_app(dataset) + +.. image:: images/dataset_zoo_coco_2017.png + :alt: dataset-zoo + :align: center + +Model Zoo +--------- + +The FiftyOne Model Zoo provides a powerful interface for downloading models and +applying them to your FiftyOne datasets. + +It provides native access to hundreds of pre-trained models, and it also +supports downloading arbitrary public or private models whose definitions are +provided via GitHub repositories or URLs. + +.. custombutton:: + :button_text: Check out the Model Zoo + :button_link: model_zoo/index.html + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model( + "clip-vit-base32-torch", + text_prompt="A photo of a", + classes=["person", "dog", "cat", "bird", "car", "tree", "chair"], + ) + + dataset.apply_model(model, label_field="zero_shot_predictions") + + session = fo.launch_app(dataset) + What's Next? ____________ @@ -407,13 +472,16 @@ us at support@voxel51.com. :hidden: Overview + FiftyOne Teams 🚀 Installation Environments Tutorials Recipes Cheat Sheets User Guide - FiftyOne Teams + Dataset Zoo + Model Zoo + FiftyOne Brain Integrations Plugins CLI diff --git a/docs/source/integrations/albumentations.rst b/docs/source/integrations/albumentations.rst index dccf834661..7ead4d9a02 100644 --- a/docs/source/integrations/albumentations.rst +++ b/docs/source/integrations/albumentations.rst @@ -94,8 +94,8 @@ Next, install the operator, selecting the Albumentations plugin from the community dropdown menu. You will also need to load (and download if necessary) a dataset to apply the -augmentations to. For this guide, we'll use the the -`quickstart dataset `_: +augmentations to. For this guide, we'll use the +:ref:`quickstart dataset `: .. code-block:: python diff --git a/docs/source/user_guide/model_zoo/api.rst b/docs/source/model_zoo/api.rst similarity index 100% rename from docs/source/user_guide/model_zoo/api.rst rename to docs/source/model_zoo/api.rst diff --git a/docs/source/user_guide/model_zoo/design.rst b/docs/source/model_zoo/design.rst similarity index 100% rename from docs/source/user_guide/model_zoo/design.rst rename to docs/source/model_zoo/design.rst diff --git a/docs/source/user_guide/model_zoo/index.rst b/docs/source/model_zoo/index.rst similarity index 100% rename from docs/source/user_guide/model_zoo/index.rst rename to docs/source/model_zoo/index.rst diff --git a/docs/source/model_zoo/models.rst b/docs/source/model_zoo/models.rst new file mode 100644 index 0000000000..a860bca372 --- /dev/null +++ b/docs/source/model_zoo/models.rst @@ -0,0 +1,8835 @@ + +.. _model-zoo-models: + +Built-In Zoo Models +=================== + +.. default-role:: code + +This page lists all of the natively available models in the FiftyOne Model Zoo. + +Check out the :ref:`API reference ` for complete instructions +for using the Model Zoo. + + +.. raw:: html + +
+ + + +
+ +
+ +
+
+ + +.. customcarditem:: + :header: alexnet-imagenet-torch + :description: AlexNet model architecture from "One weird trick for parallelizing convolutional neural networks" trained on ImageNet + :link: models.html#alexnet-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: centernet-hg104-1024-coco-tf2 + :description: CenterNet model from "Objects as Points" with the Hourglass-104 backbone trained on COCO resized to 1024x1024 + :link: models.html#centernet-hg104-1024-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: centernet-hg104-512-coco-tf2 + :description: CenterNet model from "Objects as Points" with the Hourglass-104 backbone trained on COCO resized to 512x512 + :link: models.html#centernet-hg104-512-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: centernet-mobilenet-v2-fpn-512-coco-tf2 + :description: CenterNet model from "Objects as Points" with the MobileNetV2 backbone trained on COCO resized to 512x512 + :link: models.html#centernet-mobilenet-v2-fpn-512-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: centernet-resnet101-v1-fpn-512-coco-tf2 + :description: CenterNet model from "Objects as Points" with the ResNet-101v1 backbone + FPN trained on COCO resized to 512x512 + :link: models.html#centernet-resnet101-v1-fpn-512-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: centernet-resnet50-v1-fpn-512-coco-tf2 + :description: CenterNet model from "Objects as Points" with the ResNet-50-v1 backbone + FPN trained on COCO resized to 512x512 + :link: models.html#centernet-resnet50-v1-fpn-512-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: centernet-resnet50-v2-512-coco-tf2 + :description: CenterNet model from "Objects as Points" with the ResNet-50v2 backbone trained on COCO resized to 512x512 + :link: models.html#centernet-resnet50-v2-512-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: classification-transformer-torch + :description: Hugging Face Transformers model for image classification + :link: models.html#classification-transformer-torch + :tags: Classification,Logits,Embeddings,PyTorch,Transformers + +.. customcarditem:: + :header: clip-vit-base32-torch + :description: CLIP text/image encoder from "Learning Transferable Visual Models From Natural Language Supervision" trained on 400M text-image pairs + :link: models.html#clip-vit-base32-torch + :tags: Classification,Logits,Embeddings,PyTorch,Clip,Zero-shot + +.. customcarditem:: + :header: deeplabv3-cityscapes-tf + :description: DeepLabv3+ semantic segmentation model from "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation" with Xception backbone trained on the Cityscapes dataset + :link: models.html#deeplabv3-cityscapes-tf + :tags: Segmentation,Cityscapes,TensorFlow + +.. customcarditem:: + :header: deeplabv3-mnv2-cityscapes-tf + :description: DeepLabv3+ semantic segmentation model from "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation" with MobileNetV2 backbone trained on the Cityscapes dataset + :link: models.html#deeplabv3-mnv2-cityscapes-tf + :tags: Segmentation,Cityscapes,TensorFlow + +.. customcarditem:: + :header: deeplabv3-resnet101-coco-torch + :description: DeepLabV3 model from "Rethinking Atrous Convolution for Semantic Image Segmentation" with ResNet-101 backbone trained on COCO + :link: models.html#deeplabv3-resnet101-coco-torch + :tags: Segmentation,Coco,PyTorch + +.. customcarditem:: + :header: deeplabv3-resnet50-coco-torch + :description: DeepLabV3 model from "Rethinking Atrous Convolution for Semantic Image Segmentation" with ResNet-50 backbone trained on COCO + :link: models.html#deeplabv3-resnet50-coco-torch + :tags: Segmentation,Coco,PyTorch + +.. customcarditem:: + :header: densenet121-imagenet-torch + :description: Densenet-121 model from "Densely Connected Convolutional Networks" trained on ImageNet + :link: models.html#densenet121-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: densenet161-imagenet-torch + :description: Densenet-161 model from "Densely Connected Convolutional Networks" trained on ImageNet + :link: models.html#densenet161-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: densenet169-imagenet-torch + :description: Densenet-169 model from "Densely Connected Convolutional Networks" trained on ImageNet + :link: models.html#densenet169-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: densenet201-imagenet-torch + :description: Densenet-201 model from "Densely Connected Convolutional Networks" trained on ImageNet + :link: models.html#densenet201-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: depth-estimation-transformer-torch + :description: Hugging Face Transformers model for monocular depth estimation + :link: models.html#depth-estimation-transformer-torch + :tags: Depth,PyTorch,Transformers + +.. customcarditem:: + :header: detection-transformer-torch + :description: Hugging Face Transformers model for object detection + :link: models.html#detection-transformer-torch + :tags: Detection,Logits,Embeddings,PyTorch,Transformers + +.. customcarditem:: + :header: dinov2-vitb14-torch + :description: DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-B/14 distilled + :link: models.html#dinov2-vitb14-torch + :tags: Embeddings,PyTorch + +.. customcarditem:: + :header: dinov2-vitg14-torch + :description: DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-g/14 + :link: models.html#dinov2-vitg14-torch + :tags: Embeddings,PyTorch + +.. customcarditem:: + :header: dinov2-vitl14-torch + :description: DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-L/14 distilled + :link: models.html#dinov2-vitl14-torch + :tags: Embeddings,PyTorch + +.. customcarditem:: + :header: dinov2-vits14-torch + :description: DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-S/14 distilled + :link: models.html#dinov2-vits14-torch + :tags: Embeddings,PyTorch + +.. customcarditem:: + :header: efficientdet-d0-512-coco-tf2 + :description: EfficientDet-D0 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 512x512 + :link: models.html#efficientdet-d0-512-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: efficientdet-d0-coco-tf1 + :description: EfficientDet-D0 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO + :link: models.html#efficientdet-d0-coco-tf1 + :tags: Detection,Coco,TensorFlow-1 + +.. customcarditem:: + :header: efficientdet-d1-640-coco-tf2 + :description: EfficientDet-D1 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 640x640 + :link: models.html#efficientdet-d1-640-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: efficientdet-d1-coco-tf1 + :description: EfficientDet-D1 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO + :link: models.html#efficientdet-d1-coco-tf1 + :tags: Detection,Coco,TensorFlow-1 + +.. customcarditem:: + :header: efficientdet-d2-768-coco-tf2 + :description: EfficientDet-D2 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 768x768 + :link: models.html#efficientdet-d2-768-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: efficientdet-d2-coco-tf1 + :description: EfficientDet-D2 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO + :link: models.html#efficientdet-d2-coco-tf1 + :tags: Detection,Coco,TensorFlow-1 + +.. customcarditem:: + :header: efficientdet-d3-896-coco-tf2 + :description: EfficientDet-D3 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 896x896 + :link: models.html#efficientdet-d3-896-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: efficientdet-d3-coco-tf1 + :description: EfficientDet-D3 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO + :link: models.html#efficientdet-d3-coco-tf1 + :tags: Detection,Coco,TensorFlow-1 + +.. customcarditem:: + :header: efficientdet-d4-1024-coco-tf2 + :description: EfficientDet-D4 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 1024x1024 + :link: models.html#efficientdet-d4-1024-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: efficientdet-d4-coco-tf1 + :description: EfficientDet-D4 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO + :link: models.html#efficientdet-d4-coco-tf1 + :tags: Detection,Coco,TensorFlow-1 + +.. customcarditem:: + :header: efficientdet-d5-1280-coco-tf2 + :description: EfficientDet-D5 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 1280x1280 + :link: models.html#efficientdet-d5-1280-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: efficientdet-d5-coco-tf1 + :description: EfficientDet-D5 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO + :link: models.html#efficientdet-d5-coco-tf1 + :tags: Detection,Coco,TensorFlow-1 + +.. customcarditem:: + :header: efficientdet-d6-1280-coco-tf2 + :description: EfficientDet-D6 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 1280x1280 + :link: models.html#efficientdet-d6-1280-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: efficientdet-d6-coco-tf1 + :description: EfficientDet-D6 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO + :link: models.html#efficientdet-d6-coco-tf1 + :tags: Detection,Coco,TensorFlow-1 + +.. customcarditem:: + :header: efficientdet-d7-1536-coco-tf2 + :description: EfficientDet-D7 model from "EfficientDet: Scalable and Efficient Object Detection" trained on COCO resized to 1536x1536 + :link: models.html#efficientdet-d7-1536-coco-tf2 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: faster-rcnn-inception-resnet-atrous-v2-coco-tf + :description: Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" atrous version with Inception backbone trained on COCO + :link: models.html#faster-rcnn-inception-resnet-atrous-v2-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: faster-rcnn-inception-resnet-atrous-v2-lowproposals-coco-tf + :description: Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" atrous version with low-proposals and Inception backbone trained on COCO + :link: models.html#faster-rcnn-inception-resnet-atrous-v2-lowproposals-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: faster-rcnn-inception-v2-coco-tf + :description: Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with Inception v2 backbone trained on COCO + :link: models.html#faster-rcnn-inception-v2-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: faster-rcnn-nas-coco-tf + :description: Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with NAS-net backbone trained on COCO + :link: models.html#faster-rcnn-nas-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: faster-rcnn-nas-lowproposals-coco-tf + :description: Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with low-proposals and NAS-net backbone trained on COCO + :link: models.html#faster-rcnn-nas-lowproposals-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: faster-rcnn-resnet101-coco-tf + :description: Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with ResNet-101 backbone trained on COCO + :link: models.html#faster-rcnn-resnet101-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: faster-rcnn-resnet101-lowproposals-coco-tf + :description: Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with low-proposals and ResNet-101 backbone trained on COCO + :link: models.html#faster-rcnn-resnet101-lowproposals-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: faster-rcnn-resnet50-coco-tf + :description: Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with ResNet-50 backbone trained on COCO + :link: models.html#faster-rcnn-resnet50-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: faster-rcnn-resnet50-fpn-coco-torch + :description: Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with ResNet-50 FPN backbone trained on COCO + :link: models.html#faster-rcnn-resnet50-fpn-coco-torch + :tags: Detection,Coco,PyTorch + +.. customcarditem:: + :header: faster-rcnn-resnet50-lowproposals-coco-tf + :description: Faster R-CNN model from "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" with low-proposals and ResNet-50 backbone trained on COCO + :link: models.html#faster-rcnn-resnet50-lowproposals-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: fcn-resnet101-coco-torch + :description: FCN model from "Fully Convolutional Networks for Semantic Segmentation" with ResNet-101 backbone trained on COCO + :link: models.html#fcn-resnet101-coco-torch + :tags: Segmentation,Coco,PyTorch + +.. customcarditem:: + :header: fcn-resnet50-coco-torch + :description: FCN model from "Fully Convolutional Networks for Semantic Segmentation" with ResNet-50 backbone trained on COCO + :link: models.html#fcn-resnet50-coco-torch + :tags: Segmentation,Coco,PyTorch + +.. customcarditem:: + :header: googlenet-imagenet-torch + :description: GoogLeNet (Inception v1) model from "Going Deeper with Convolutions" trained on ImageNet + :link: models.html#googlenet-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: inception-resnet-v2-imagenet-tf1 + :description: Inception v2 model from "Rethinking the Inception Architecture for Computer Vision" trained on ImageNet + :link: models.html#inception-resnet-v2-imagenet-tf1 + :tags: Classification,Embeddings,Logits,Imagenet,TensorFlow-1 + +.. customcarditem:: + :header: inception-v3-imagenet-torch + :description: Inception v3 model from "Rethinking the Inception Architecture for Computer Vision" trained on ImageNet + :link: models.html#inception-v3-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: inception-v4-imagenet-tf1 + :description: Inception v4 model from "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning" trained on ImageNet + :link: models.html#inception-v4-imagenet-tf1 + :tags: Classification,Embeddings,Logits,Imagenet,TensorFlow-1 + +.. customcarditem:: + :header: keypoint-rcnn-resnet50-fpn-coco-torch + :description: Keypoint R-CNN model from "Mask R-CNN" with ResNet-50 FPN backbone trained on COCO + :link: models.html#keypoint-rcnn-resnet50-fpn-coco-torch + :tags: Keypoints,Coco,PyTorch + +.. customcarditem:: + :header: mask-rcnn-inception-resnet-v2-atrous-coco-tf + :description: Mask R-CNN model from "Mask R-CNN" atrous version with Inception backbone trained on COCO + :link: models.html#mask-rcnn-inception-resnet-v2-atrous-coco-tf + :tags: Instances,Coco,TensorFlow + +.. customcarditem:: + :header: mask-rcnn-inception-v2-coco-tf + :description: Mask R-CNN model from "Mask R-CNN" with Inception backbone trained on COCO + :link: models.html#mask-rcnn-inception-v2-coco-tf + :tags: Instances,Coco,TensorFlow + +.. customcarditem:: + :header: mask-rcnn-resnet101-atrous-coco-tf + :description: Mask R-CNN model from "Mask R-CNN" atrous version with ResNet-101 backbone trained on COCO + :link: models.html#mask-rcnn-resnet101-atrous-coco-tf + :tags: Instances,Coco,TensorFlow + +.. customcarditem:: + :header: mask-rcnn-resnet50-atrous-coco-tf + :description: Mask R-CNN model from "Mask R-CNN" atrous version with ResNet-50 backbone trained on COCO + :link: models.html#mask-rcnn-resnet50-atrous-coco-tf + :tags: Instances,Coco,TensorFlow + +.. customcarditem:: + :header: mask-rcnn-resnet50-fpn-coco-torch + :description: Mask R-CNN model from "Mask R-CNN" with ResNet-50 FPN backbone trained on COCO + :link: models.html#mask-rcnn-resnet50-fpn-coco-torch + :tags: Instances,Coco,PyTorch + +.. customcarditem:: + :header: mnasnet0.5-imagenet-torch + :description: MNASNet model from from "MnasNet: Platform-Aware Neural Architecture Search for Mobile" with depth multiplier of 0.5 trained on ImageNet + :link: models.html#mnasnet0.5-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: mnasnet1.0-imagenet-torch + :description: MNASNet model from "MnasNet: Platform-Aware Neural Architecture Search for Mobile" with depth multiplier of 1.0 trained on ImageNet + :link: models.html#mnasnet1.0-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: mobilenet-v2-imagenet-tf1 + :description: MobileNetV2 model from "MobileNetV2: Inverted Residuals and Linear Bottlenecks" trained on ImageNet + :link: models.html#mobilenet-v2-imagenet-tf1 + :tags: Classification,Embeddings,Logits,Imagenet,TensorFlow-1 + +.. customcarditem:: + :header: mobilenet-v2-imagenet-torch + :description: MobileNetV2 model from "MobileNetV2: Inverted Residuals and Linear Bottlenecks" trained on ImageNet + :link: models.html#mobilenet-v2-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: open-clip-torch + :description: OPEN CLIP text/image encoder from "Learning Transferable Visual Models From Natural Language Supervision" trained on 400M text-image pairs + :link: models.html#open-clip-torch + :tags: Classification,Logits,Embeddings,PyTorch,Clip,Zero-shot + +.. customcarditem:: + :header: resnet-v1-50-imagenet-tf1 + :description: ResNet-50 v1 model from "Deep Residual Learning for Image Recognition" trained on ImageNet + :link: models.html#resnet-v1-50-imagenet-tf1 + :tags: Classification,Embeddings,Logits,Imagenet,TensorFlow-1 + +.. customcarditem:: + :header: resnet-v2-50-imagenet-tf1 + :description: ResNet-50 v2 model from "Deep Residual Learning for Image Recognition" trained on ImageNet + :link: models.html#resnet-v2-50-imagenet-tf1 + :tags: Classification,Embeddings,Logits,Imagenet,TensorFlow-1 + +.. customcarditem:: + :header: resnet101-imagenet-torch + :description: ResNet-101 model from "Deep Residual Learning for Image Recognition" trained on ImageNet + :link: models.html#resnet101-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: resnet152-imagenet-torch + :description: ResNet-152 model from "Deep Residual Learning for Image Recognition" trained on ImageNet + :link: models.html#resnet152-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: resnet18-imagenet-torch + :description: ResNet-18 model from "Deep Residual Learning for Image Recognition" trained on ImageNet + :link: models.html#resnet18-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: resnet34-imagenet-torch + :description: ResNet-34 model from "Deep Residual Learning for Image Recognition" trained on ImageNet + :link: models.html#resnet34-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: resnet50-imagenet-torch + :description: ResNet-50 model from "Deep Residual Learning for Image Recognition" trained on ImageNet + :link: models.html#resnet50-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: resnext101-32x8d-imagenet-torch + :description: ResNeXt-101 32x8d model from "Aggregated Residual Transformations for Deep Neural Networks" trained on ImageNet + :link: models.html#resnext101-32x8d-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: resnext50-32x4d-imagenet-torch + :description: ResNeXt-50 32x4d model from "Aggregated Residual Transformations for Deep Neural Networks" trained on ImageNet + :link: models.html#resnext50-32x4d-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: retinanet-resnet50-fpn-coco-torch + :description: RetinaNet model from "Focal Loss for Dense Object Detection" with ResNet-50 FPN backbone trained on COCO + :link: models.html#retinanet-resnet50-fpn-coco-torch + :tags: Detection,Coco,PyTorch + +.. customcarditem:: + :header: rfcn-resnet101-coco-tf + :description: R-FCN object detection model from "R-FCN: Object Detection via Region-based Fully Convolutional Networks" with ResNet-101 backbone trained on COCO + :link: models.html#rfcn-resnet101-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: rtdetr-l-coco-torch + :description: RT-DETR-l model trained on COCO + :link: models.html#rtdetr-l-coco-torch + :tags: Detection,Coco,PyTorch,Transformer + +.. customcarditem:: + :header: rtdetr-x-coco-torch + :description: RT-DETR-x model trained on COCO + :link: models.html#rtdetr-x-coco-torch + :tags: Detection,Coco,PyTorch,Transformer + +.. customcarditem:: + :header: segment-anything-2-hiera-base-plus-image-torch + :description: Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos" + :link: models.html#segment-anything-2-hiera-base-plus-image-torch + :tags: Segment-anything,PyTorch,Zero-shot + +.. customcarditem:: + :header: segment-anything-2-hiera-base-plus-video-torch + :description: Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos" + :link: models.html#segment-anything-2-hiera-base-plus-video-torch + :tags: Segment-anything,PyTorch,Zero-shot,Video + +.. customcarditem:: + :header: segment-anything-2-hiera-large-image-torch + :description: Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos" + :link: models.html#segment-anything-2-hiera-large-image-torch + :tags: Segment-anything,PyTorch,Zero-shot + +.. customcarditem:: + :header: segment-anything-2-hiera-large-video-torch + :description: Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos" + :link: models.html#segment-anything-2-hiera-large-video-torch + :tags: Segment-anything,PyTorch,Zero-shot,Video + +.. customcarditem:: + :header: segment-anything-2-hiera-small-image-torch + :description: Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos" + :link: models.html#segment-anything-2-hiera-small-image-torch + :tags: Segment-anything,PyTorch,Zero-shot + +.. customcarditem:: + :header: segment-anything-2-hiera-small-video-torch + :description: Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos" + :link: models.html#segment-anything-2-hiera-small-video-torch + :tags: Segment-anything,PyTorch,Zero-shot,Video + +.. customcarditem:: + :header: segment-anything-2-hiera-tiny-image-torch + :description: Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos" + :link: models.html#segment-anything-2-hiera-tiny-image-torch + :tags: Segment-anything,PyTorch,Zero-shot + +.. customcarditem:: + :header: segment-anything-2-hiera-tiny-video-torch + :description: Segment Anything Model 2 (SAM2) from "SAM2: Segment Anything in Images and Videos" + :link: models.html#segment-anything-2-hiera-tiny-video-torch + :tags: Segment-anything,PyTorch,Zero-shot,Video + +.. customcarditem:: + :header: segment-anything-vitb-torch + :description: Segment Anything Model (SAM) from "Segment Anything" with ViT-B/16 backbone trained on SA-1B + :link: models.html#segment-anything-vitb-torch + :tags: Segment-anything,Sa-1b,PyTorch,Zero-shot + +.. customcarditem:: + :header: segment-anything-vith-torch + :description: Segment Anything Model (SAM) from "Segment Anything" with ViT-H/16 backbone trained on SA-1B + :link: models.html#segment-anything-vith-torch + :tags: Segment-anything,Sa-1b,PyTorch,Zero-shot + +.. customcarditem:: + :header: segment-anything-vitl-torch + :description: Segment Anything Model (SAM) from "Segment Anything" with ViT-L/16 backbone trained on SA-1B + :link: models.html#segment-anything-vitl-torch + :tags: Segment-anything,Sa-1b,PyTorch,Zero-shot + +.. customcarditem:: + :header: segmentation-transformer-torch + :description: Hugging Face Transformers model for semantic segmentation + :link: models.html#segmentation-transformer-torch + :tags: Segmentation,PyTorch,Transformers + +.. customcarditem:: + :header: shufflenetv2-0.5x-imagenet-torch + :description: ShuffleNetV2 model from "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" with 0.5x output channels trained on ImageNet + :link: models.html#shufflenetv2-0.5x-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: shufflenetv2-1.0x-imagenet-torch + :description: ShuffleNetV2 model from "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" with 1.0x output channels trained on ImageNet + :link: models.html#shufflenetv2-1.0x-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: squeezenet-1.1-imagenet-torch + :description: SqueezeNet 1.1 model from "the official SqueezeNet repo" trained on ImageNet + :link: models.html#squeezenet-1.1-imagenet-torch + :tags: Classification,Imagenet,PyTorch + +.. customcarditem:: + :header: squeezenet-imagenet-torch + :description: SqueezeNet model from "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and" trained on ImageNet + :link: models.html#squeezenet-imagenet-torch + :tags: Classification,Imagenet,PyTorch + +.. customcarditem:: + :header: ssd-inception-v2-coco-tf + :description: Inception Single Shot Detector model from "SSD: Single Shot MultiBox Detector" trained on COCO + :link: models.html#ssd-inception-v2-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: ssd-mobilenet-v1-coco-tf + :description: Single Shot Detector model from "SSD: Single Shot MultiBox Detector" with MobileNetV1 backbone trained on COCO + :link: models.html#ssd-mobilenet-v1-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: ssd-mobilenet-v1-fpn-640-coco17 + :description: MobileNetV1 model from "MobileNetV2: Inverted Residuals and Linear Bottlenecks" resized to 640x640 + :link: models.html#ssd-mobilenet-v1-fpn-640-coco17 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: ssd-mobilenet-v1-fpn-coco-tf + :description: FPN Single Shot Detector model from "SSD: Single Shot MultiBox Detector" with MobileNetV1 backbone trained on COCO + :link: models.html#ssd-mobilenet-v1-fpn-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: ssd-mobilenet-v2-320-coco17 + :description: MobileNetV2 model from "MobileNetV2: Inverted Residuals and Linear Bottlenecks" resized to 320x320 + :link: models.html#ssd-mobilenet-v2-320-coco17 + :tags: Detection,Coco,TensorFlow-2 + +.. customcarditem:: + :header: ssd-resnet50-fpn-coco-tf + :description: FPN Single Shot Detector model from "SSD: Single Shot MultiBox Detector" with ResNet-50 backbone trained on COCO + :link: models.html#ssd-resnet50-fpn-coco-tf + :tags: Detection,Coco,TensorFlow + +.. customcarditem:: + :header: vgg11-bn-imagenet-torch + :description: VGG-11 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" with batch normalization trained on ImageNet + :link: models.html#vgg11-bn-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: vgg11-imagenet-torch + :description: VGG-11 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" trained on ImageNet + :link: models.html#vgg11-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: vgg13-bn-imagenet-torch + :description: VGG-13 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" with batch normalization trained on ImageNet + :link: models.html#vgg13-bn-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: vgg13-imagenet-torch + :description: VGG-13 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" trained on ImageNet + :link: models.html#vgg13-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: vgg16-bn-imagenet-torch + :description: VGG-16 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" with batch normalization trained on ImageNet + :link: models.html#vgg16-bn-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: vgg16-imagenet-tf1 + :description: VGG-16 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" trained on ImageNet + :link: models.html#vgg16-imagenet-tf1 + :tags: Classification,Embeddings,Logits,Imagenet,TensorFlow-1 + +.. customcarditem:: + :header: vgg16-imagenet-torch + :description: VGG-16 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" trained on ImageNet + :link: models.html#vgg16-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: vgg19-bn-imagenet-torch + :description: VGG-19 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" with batch normalization trained on ImageNet + :link: models.html#vgg19-bn-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: vgg19-imagenet-torch + :description: VGG-19 model from "Very Deep Convolutional Networks for Large-Scale Image Recognition" trained on ImageNet + :link: models.html#vgg19-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: wide-resnet101-2-imagenet-torch + :description: Wide ResNet-101-2 model from "Wide Residual Networks" trained on ImageNet + :link: models.html#wide-resnet101-2-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: wide-resnet50-2-imagenet-torch + :description: Wide ResNet-50-2 model from "Wide Residual Networks" trained on ImageNet + :link: models.html#wide-resnet50-2-imagenet-torch + :tags: Classification,Embeddings,Logits,Imagenet,PyTorch + +.. customcarditem:: + :header: yolo-nas-torch + :description: YOLO-NAS is an open-source training library for advanced computer vision models. It specializes in accuracy and efficiency, supporting tasks like object detection + :link: models.html#yolo-nas-torch + :tags: Classification,PyTorch,Yolo + +.. customcarditem:: + :header: yolo-v2-coco-tf1 + :description: YOLOv2 model from "YOLO9000: Better, Faster, Stronger" trained on COCO + :link: models.html#yolo-v2-coco-tf1 + :tags: Detection,Coco,TensorFlow-1 + +.. customcarditem:: + :header: yolov10l-coco-torch + :description: YOLOv10-L model trained on COCO + :link: models.html#yolov10l-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov10m-coco-torch + :description: YOLOv10-M model trained on COCO + :link: models.html#yolov10m-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov10n-coco-torch + :description: YOLOv10-N model trained on COCO + :link: models.html#yolov10n-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov10s-coco-torch + :description: YOLOv10-S model trained on COCO + :link: models.html#yolov10s-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov10x-coco-torch + :description: YOLOv10-X model trained on COCO + :link: models.html#yolov10x-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov5l-coco-torch + :description: Ultralytics YOLOv5l model trained on COCO + :link: models.html#yolov5l-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov5m-coco-torch + :description: Ultralytics YOLOv5m model trained on COCO + :link: models.html#yolov5m-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov5n-coco-torch + :description: Ultralytics YOLOv5n model trained on COCO + :link: models.html#yolov5n-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov5s-coco-torch + :description: Ultralytics YOLOv5s model trained on COCO + :link: models.html#yolov5s-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov5x-coco-torch + :description: Ultralytics YOLOv5x model trained on COCO + :link: models.html#yolov5x-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8l-coco-torch + :description: Ultralytics YOLOv8l model trained on COCO + :link: models.html#yolov8l-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8l-obb-dotav1-torch + :description: YOLOv8l Oriented Bounding Box model + :link: models.html#yolov8l-obb-dotav1-torch + :tags: Detection,PyTorch,Yolo,Polylines,Obb + +.. customcarditem:: + :header: yolov8l-oiv7-torch + :description: Ultralytics YOLOv8l model trained Open Images v7 + :link: models.html#yolov8l-oiv7-torch + :tags: Detection,Oiv7,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8l-seg-coco-torch + :description: Ultralytics YOLOv8l Segmentation model trained on COCO + :link: models.html#yolov8l-seg-coco-torch + :tags: Segmentation,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8l-world-torch + :description: YOLOv8l-World model + :link: models.html#yolov8l-world-torch + :tags: Detection,PyTorch,Yolo,Zero-shot + +.. customcarditem:: + :header: yolov8m-coco-torch + :description: Ultralytics YOLOv8m model trained on COCO + :link: models.html#yolov8m-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8m-obb-dotav1-torch + :description: YOLOv8m Oriented Bounding Box model + :link: models.html#yolov8m-obb-dotav1-torch + :tags: Detection,PyTorch,Yolo,Polylines,Obb + +.. customcarditem:: + :header: yolov8m-oiv7-torch + :description: Ultralytics YOLOv8m model trained Open Images v7 + :link: models.html#yolov8m-oiv7-torch + :tags: Detection,Oiv7,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8m-seg-coco-torch + :description: Ultralytics YOLOv8m Segmentation model trained on COCO + :link: models.html#yolov8m-seg-coco-torch + :tags: Segmentation,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8m-world-torch + :description: YOLOv8m-World model + :link: models.html#yolov8m-world-torch + :tags: Detection,PyTorch,Yolo,Zero-shot + +.. customcarditem:: + :header: yolov8n-coco-torch + :description: Ultralytics YOLOv8n model trained on COCO + :link: models.html#yolov8n-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8n-obb-dotav1-torch + :description: YOLOv8n Oriented Bounding Box model + :link: models.html#yolov8n-obb-dotav1-torch + :tags: Detection,PyTorch,Yolo,Polylines,Obb + +.. customcarditem:: + :header: yolov8n-oiv7-torch + :description: Ultralytics YOLOv8n model trained on Open Images v7 + :link: models.html#yolov8n-oiv7-torch + :tags: Detection,Oiv7,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8n-seg-coco-torch + :description: Ultralytics YOLOv8n Segmentation model trained on COCO + :link: models.html#yolov8n-seg-coco-torch + :tags: Segmentation,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8s-coco-torch + :description: Ultralytics YOLOv8s model trained on COCO + :link: models.html#yolov8s-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8s-obb-dotav1-torch + :description: YOLOv8s Oriented Bounding Box model + :link: models.html#yolov8s-obb-dotav1-torch + :tags: Detection,PyTorch,Yolo,Polylines,Obb + +.. customcarditem:: + :header: yolov8s-oiv7-torch + :description: Ultralytics YOLOv8s model trained on Open Images v7 + :link: models.html#yolov8s-oiv7-torch + :tags: Detection,Oiv7,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8s-seg-coco-torch + :description: Ultralytics YOLOv8s Segmentation model trained on COCO + :link: models.html#yolov8s-seg-coco-torch + :tags: Segmentation,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8s-world-torch + :description: YOLOv8s-World model + :link: models.html#yolov8s-world-torch + :tags: Detection,PyTorch,Yolo,Zero-shot + +.. customcarditem:: + :header: yolov8x-coco-torch + :description: Ultralytics YOLOv8x model trained on COCO + :link: models.html#yolov8x-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8x-obb-dotav1-torch + :description: YOLOv8x Oriented Bounding Box model + :link: models.html#yolov8x-obb-dotav1-torch + :tags: Detection,PyTorch,Yolo,Polylines,Obb + +.. customcarditem:: + :header: yolov8x-oiv7-torch + :description: Ultralytics YOLOv8x model trained Open Images v7 + :link: models.html#yolov8x-oiv7-torch + :tags: Detection,Oiv7,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8x-seg-coco-torch + :description: Ultralytics YOLOv8x Segmentation model trained on COCO + :link: models.html#yolov8x-seg-coco-torch + :tags: Segmentation,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov8x-world-torch + :description: YOLOv8x-World model + :link: models.html#yolov8x-world-torch + :tags: Detection,PyTorch,Yolo,Zero-shot + +.. customcarditem:: + :header: yolov9c-coco-torch + :description: YOLOv9-C model trained on COCO + :link: models.html#yolov9c-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov9c-seg-coco-torch + :description: YOLOv9-C Segmentation model trained on COCO + :link: models.html#yolov9c-seg-coco-torch + :tags: Segmentation,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov9e-coco-torch + :description: YOLOv9-E model trained on COCO + :link: models.html#yolov9e-coco-torch + :tags: Detection,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: yolov9e-seg-coco-torch + :description: YOLOv9-E Segmentation model trained on COCO + :link: models.html#yolov9e-seg-coco-torch + :tags: Segmentation,Coco,PyTorch,Yolo + +.. customcarditem:: + :header: zero-shot-classification-transformer-torch + :description: Hugging Face Transformers model for zero-shot image classification + :link: models.html#zero-shot-classification-transformer-torch + :tags: Classification,Logits,Embeddings,PyTorch,Transformers,Zero-shot + +.. customcarditem:: + :header: zero-shot-detection-transformer-torch + :description: Hugging Face Transformers model for zero-shot object detection + :link: models.html#zero-shot-detection-transformer-torch + :tags: Detection,Logits,Embeddings,PyTorch,Transformers,Zero-shot + +.. raw:: html + +
+ + + +
+ +
+ + +.. _model-zoo-torch-models: + +Torch models +------------ + +.. _model-zoo-alexnet-imagenet-torch: + +alexnet-imagenet-torch +______________________ + +AlexNet model architecture from `One weird trick for parallelizing convolutional neural networks `_ trained on ImageNet. + +**Details** + +- Model name: ``alexnet-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 233.10 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("alexnet-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-classification-transformer-torch: + +classification-transformer-torch +________________________________ + +Hugging Face Transformers model for image classification. + +**Details** + +- Model name: ``classification-transformer-torch`` +- Model source: https://huggingface.co/docs/transformers/tasks/image_classification +- Exposes embeddings? yes +- Tags: ``classification, logits, embeddings, torch, transformers`` + +**Requirements** + +- Packages: ``torch, torchvision, transformers`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("classification-transformer-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-clip-vit-base32-torch: + +clip-vit-base32-torch +_____________________ + +CLIP text/image encoder from `Learning Transferable Visual Models From Natural Language Supervision `_ trained on 400M text-image pairs. + +**Details** + +- Model name: ``clip-vit-base32-torch`` +- Model source: https://github.com/openai/CLIP +- Model size: 337.58 MB +- Exposes embeddings? yes +- Tags: ``classification, logits, embeddings, torch, clip, zero-shot`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("clip-vit-base32-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + # + # Make zero-shot predictions with custom classes + # + + model = foz.load_zoo_model( + "clip-vit-base32-torch", + text_prompt="A photo of a", + classes=["person", "dog", "cat", "bird", "car", "tree", "chair"], + ) + + dataset.apply_model(model, label_field="predictions") + session.refresh() + + +.. _model-zoo-deeplabv3-resnet101-coco-torch: + +deeplabv3-resnet101-coco-torch +______________________________ + +DeepLabV3 model from `Rethinking Atrous Convolution for Semantic Image Segmentation `_ with ResNet-101 backbone trained on COCO. + +**Details** + +- Model name: ``deeplabv3-resnet101-coco-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 233.22 MB +- Exposes embeddings? no +- Tags: ``segmentation, coco, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("deeplabv3-resnet101-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-deeplabv3-resnet50-coco-torch: + +deeplabv3-resnet50-coco-torch +_____________________________ + +DeepLabV3 model from `Rethinking Atrous Convolution for Semantic Image Segmentation `_ with ResNet-50 backbone trained on COCO. + +**Details** + +- Model name: ``deeplabv3-resnet50-coco-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 160.51 MB +- Exposes embeddings? no +- Tags: ``segmentation, coco, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("deeplabv3-resnet50-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-densenet121-imagenet-torch: + +densenet121-imagenet-torch +__________________________ + +Densenet-121 model from `Densely Connected Convolutional Networks `_ trained on ImageNet. + +**Details** + +- Model name: ``densenet121-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 30.84 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("densenet121-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-densenet161-imagenet-torch: + +densenet161-imagenet-torch +__________________________ + +Densenet-161 model from `Densely Connected Convolutional Networks `_ trained on ImageNet. + +**Details** + +- Model name: ``densenet161-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 110.37 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("densenet161-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-densenet169-imagenet-torch: + +densenet169-imagenet-torch +__________________________ + +Densenet-169 model from `Densely Connected Convolutional Networks `_ trained on ImageNet. + +**Details** + +- Model name: ``densenet169-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 54.71 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("densenet169-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-densenet201-imagenet-torch: + +densenet201-imagenet-torch +__________________________ + +Densenet-201 model from `Densely Connected Convolutional Networks `_ trained on ImageNet. + +**Details** + +- Model name: ``densenet201-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 77.37 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("densenet201-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-depth-estimation-transformer-torch: + +depth-estimation-transformer-torch +__________________________________ + +Hugging Face Transformers model for monocular depth estimation. + +**Details** + +- Model name: ``depth-estimation-transformer-torch`` +- Model source: https://huggingface.co/docs/transformers/tasks/monocular_depth_estimation +- Exposes embeddings? no +- Tags: ``depth, torch, transformers`` + +**Requirements** + +- Packages: ``torch, torchvision, transformers`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("depth-estimation-transformer-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-detection-transformer-torch: + +detection-transformer-torch +___________________________ + +Hugging Face Transformers model for object detection. + +**Details** + +- Model name: ``detection-transformer-torch`` +- Model source: https://huggingface.co/docs/transformers/tasks/object_detection +- Exposes embeddings? yes +- Tags: ``detection, logits, embeddings, torch, transformers`` + +**Requirements** + +- Packages: ``torch, torchvision, transformers`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("detection-transformer-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-dinov2-vitb14-torch: + +dinov2-vitb14-torch +___________________ + +DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-B/14 distilled. + +**Details** + +- Model name: ``dinov2-vitb14-torch`` +- Model source: https://github.com/facebookresearch/dinov2 +- Model size: 330.33 MB +- Exposes embeddings? yes +- Tags: ``embeddings, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("dinov2-vitb14-torch") + + embeddings = dataset.compute_embeddings(model) + + + +.. _model-zoo-dinov2-vitg14-torch: + +dinov2-vitg14-torch +___________________ + +DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-g/14. + +**Details** + +- Model name: ``dinov2-vitg14-torch`` +- Model source: https://github.com/facebookresearch/dinov2 +- Model size: 4.23 GB +- Exposes embeddings? yes +- Tags: ``embeddings, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("dinov2-vitg14-torch") + + embeddings = dataset.compute_embeddings(model) + + + +.. _model-zoo-dinov2-vitl14-torch: + +dinov2-vitl14-torch +___________________ + +DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-L/14 distilled. + +**Details** + +- Model name: ``dinov2-vitl14-torch`` +- Model source: https://github.com/facebookresearch/dinov2 +- Model size: 1.13 GB +- Exposes embeddings? yes +- Tags: ``embeddings, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("dinov2-vitl14-torch") + + embeddings = dataset.compute_embeddings(model) + + + +.. _model-zoo-dinov2-vits14-torch: + +dinov2-vits14-torch +___________________ + +DINOv2: Learning Robust Visual Features without Supervision. Model: ViT-S/14 distilled. + +**Details** + +- Model name: ``dinov2-vits14-torch`` +- Model source: https://github.com/facebookresearch/dinov2 +- Model size: 84.19 MB +- Exposes embeddings? yes +- Tags: ``embeddings, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("dinov2-vits14-torch") + + embeddings = dataset.compute_embeddings(model) + + + +.. _model-zoo-faster-rcnn-resnet50-fpn-coco-torch: + +faster-rcnn-resnet50-fpn-coco-torch +___________________________________ + +Faster R-CNN model from `Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks `_ with ResNet-50 FPN backbone trained on COCO. + +**Details** + +- Model name: ``faster-rcnn-resnet50-fpn-coco-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 159.74 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("faster-rcnn-resnet50-fpn-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-fcn-resnet101-coco-torch: + +fcn-resnet101-coco-torch +________________________ + +FCN model from `Fully Convolutional Networks for Semantic Segmentation `_ with ResNet-101 backbone trained on COCO. + +**Details** + +- Model name: ``fcn-resnet101-coco-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 207.71 MB +- Exposes embeddings? no +- Tags: ``segmentation, coco, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("fcn-resnet101-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-fcn-resnet50-coco-torch: + +fcn-resnet50-coco-torch +_______________________ + +FCN model from `Fully Convolutional Networks for Semantic Segmentation `_ with ResNet-50 backbone trained on COCO. + +**Details** + +- Model name: ``fcn-resnet50-coco-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 135.01 MB +- Exposes embeddings? no +- Tags: ``segmentation, coco, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("fcn-resnet50-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-googlenet-imagenet-torch: + +googlenet-imagenet-torch +________________________ + +GoogLeNet (Inception v1) model from `Going Deeper with Convolutions `_ trained on ImageNet. + +**Details** + +- Model name: ``googlenet-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 49.73 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``scipy, torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("googlenet-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-inception-v3-imagenet-torch: + +inception-v3-imagenet-torch +___________________________ + +Inception v3 model from `Rethinking the Inception Architecture for Computer Vision `_ trained on ImageNet. + +**Details** + +- Model name: ``inception-v3-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 103.81 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``scipy, torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("inception-v3-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-keypoint-rcnn-resnet50-fpn-coco-torch: + +keypoint-rcnn-resnet50-fpn-coco-torch +_____________________________________ + +Keypoint R-CNN model from `Mask R-CNN `_ with ResNet-50 FPN backbone trained on COCO. + +**Details** + +- Model name: ``keypoint-rcnn-resnet50-fpn-coco-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 226.05 MB +- Exposes embeddings? no +- Tags: ``keypoints, coco, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("keypoint-rcnn-resnet50-fpn-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-mask-rcnn-resnet50-fpn-coco-torch: + +mask-rcnn-resnet50-fpn-coco-torch +_________________________________ + +Mask R-CNN model from `Mask R-CNN `_ with ResNet-50 FPN backbone trained on COCO. + +**Details** + +- Model name: ``mask-rcnn-resnet50-fpn-coco-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 169.84 MB +- Exposes embeddings? no +- Tags: ``instances, coco, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("mask-rcnn-resnet50-fpn-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-mnasnet0.5-imagenet-torch: + +mnasnet0.5-imagenet-torch +_________________________ + +MNASNet model from from `MnasNet: Platform-Aware Neural Architecture Search for Mobile `_ with depth multiplier of 0.5 trained on ImageNet. + +**Details** + +- Model name: ``mnasnet0.5-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 8.59 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("mnasnet0.5-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-mnasnet1.0-imagenet-torch: + +mnasnet1.0-imagenet-torch +_________________________ + +MNASNet model from `MnasNet: Platform-Aware Neural Architecture Search for Mobile `_ with depth multiplier of 1.0 trained on ImageNet. + +**Details** + +- Model name: ``mnasnet1.0-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 16.92 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("mnasnet1.0-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-mobilenet-v2-imagenet-torch: + +mobilenet-v2-imagenet-torch +___________________________ + +MobileNetV2 model from `MobileNetV2: Inverted Residuals and Linear Bottlenecks `_ trained on ImageNet. + +**Details** + +- Model name: ``mobilenet-v2-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 13.55 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("mobilenet-v2-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-open-clip-torch: + +open-clip-torch +_______________ + +OPEN CLIP text/image encoder from `Learning Transferable Visual Models From Natural Language Supervision `_ trained on 400M text-image pairs. + +**Details** + +- Model name: ``open-clip-torch`` +- Model source: https://github.com/mlfoundations/open_clip +- Exposes embeddings? yes +- Tags: ``classification, logits, embeddings, torch, clip, zero-shot`` + +**Requirements** + +- Packages: ``torch, torchvision, open_clip_torch`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("open-clip-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + # + # Make zero-shot predictions with custom classes + # + + model = foz.load_zoo_model( + "open-clip-torch", + text_prompt="A photo of a", + classes=["person", "dog", "cat", "bird", "car", "tree", "chair"], + ) + + dataset.apply_model(model, label_field="predictions") + session.refresh() + + +.. _model-zoo-resnet101-imagenet-torch: + +resnet101-imagenet-torch +________________________ + +ResNet-101 model from `Deep Residual Learning for Image Recognition `_ trained on ImageNet. + +**Details** + +- Model name: ``resnet101-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 170.45 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("resnet101-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-resnet152-imagenet-torch: + +resnet152-imagenet-torch +________________________ + +ResNet-152 model from `Deep Residual Learning for Image Recognition `_ trained on ImageNet. + +**Details** + +- Model name: ``resnet152-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 230.34 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("resnet152-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-resnet18-imagenet-torch: + +resnet18-imagenet-torch +_______________________ + +ResNet-18 model from `Deep Residual Learning for Image Recognition `_ trained on ImageNet. + +**Details** + +- Model name: ``resnet18-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 44.66 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("resnet18-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-resnet34-imagenet-torch: + +resnet34-imagenet-torch +_______________________ + +ResNet-34 model from `Deep Residual Learning for Image Recognition `_ trained on ImageNet. + +**Details** + +- Model name: ``resnet34-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 83.26 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("resnet34-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-resnet50-imagenet-torch: + +resnet50-imagenet-torch +_______________________ + +ResNet-50 model from `Deep Residual Learning for Image Recognition `_ trained on ImageNet. + +**Details** + +- Model name: ``resnet50-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 97.75 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("resnet50-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-resnext101-32x8d-imagenet-torch: + +resnext101-32x8d-imagenet-torch +_______________________________ + +ResNeXt-101 32x8d model from `Aggregated Residual Transformations for Deep Neural Networks `_ trained on ImageNet. + +**Details** + +- Model name: ``resnext101-32x8d-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 339.59 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("resnext101-32x8d-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-resnext50-32x4d-imagenet-torch: + +resnext50-32x4d-imagenet-torch +______________________________ + +ResNeXt-50 32x4d model from `Aggregated Residual Transformations for Deep Neural Networks `_ trained on ImageNet. + +**Details** + +- Model name: ``resnext50-32x4d-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 95.79 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("resnext50-32x4d-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-retinanet-resnet50-fpn-coco-torch: + +retinanet-resnet50-fpn-coco-torch +_________________________________ + +RetinaNet model from `Focal Loss for Dense Object Detection `_ with ResNet-50 FPN backbone trained on COCO. + +**Details** + +- Model name: ``retinanet-resnet50-fpn-coco-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 130.27 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch`` + +**Requirements** + +- Packages: ``torch, torchvision>=0.8.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("retinanet-resnet50-fpn-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-rtdetr-l-coco-torch: + +rtdetr-l-coco-torch +___________________ + +RT-DETR-l model trained on COCO. + +**Details** + +- Model name: ``rtdetr-l-coco-torch`` +- Model source: https://docs.ultralytics.com/models/rtdetr/ +- Model size: 63.43 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, transformer`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("rtdetr-l-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-rtdetr-x-coco-torch: + +rtdetr-x-coco-torch +___________________ + +RT-DETR-x model trained on COCO. + +**Details** + +- Model name: ``rtdetr-x-coco-torch`` +- Model source: https://docs.ultralytics.com/models/rtdetr/ +- Model size: 129.47 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, transformer`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("rtdetr-x-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-segment-anything-2-hiera-base-plus-image-torch: + +segment-anything-2-hiera-base-plus-image-torch +______________________________________________ + +Segment Anything Model 2 (SAM2) from `SAM2: Segment Anything in Images and Videos `_. + +**Details** + +- Model name: ``segment-anything-2-hiera-base-plus-image-torch`` +- Model source: https://ai.meta.com/sam2/ +- Model size: 148.68 MB +- Exposes embeddings? no +- Tags: ``segment-anything, torch, zero-shot`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("segment-anything-2-hiera-base-plus-image-torch") + + # Segment inside boxes + dataset.apply_model( + model, + label_field="segmentations", + prompt_field="ground_truth", # can contain Detections or Keypoints + ) + + # Full automatic segmentations + dataset.apply_model(model, label_field="auto") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-segment-anything-2-hiera-base-plus-video-torch: + +segment-anything-2-hiera-base-plus-video-torch +______________________________________________ + +Segment Anything Model 2 (SAM2) from `SAM2: Segment Anything in Images and Videos `_. + +**Details** + +- Model name: ``segment-anything-2-hiera-base-plus-video-torch`` +- Model source: https://ai.meta.com/sam2/ +- Model size: 148.68 MB +- Exposes embeddings? no +- Tags: ``segment-anything, torch, zero-shot, video`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + from fiftyone import ViewField as F + + dataset = foz.load_zoo_dataset("quickstart-video", max_samples=2) + + # Only retain detections in the first frame + ( + dataset + .match_frames(F("frame_number") > 1) + .set_field("frames.detections", None) + .save() + ) + + model = foz.load_zoo_model("segment-anything-2-hiera-base-plus-video-torch") + + # Segment inside boxes and propagate to all frames + dataset.apply_model( + model, + label_field="segmentations", + prompt_field="frames.detections", # can contain Detections or Keypoints + ) + + session = fo.launch_app(dataset) + + + +.. _model-zoo-segment-anything-2-hiera-large-image-torch: + +segment-anything-2-hiera-large-image-torch +__________________________________________ + +Segment Anything Model 2 (SAM2) from `SAM2: Segment Anything in Images and Videos `_. + +**Details** + +- Model name: ``segment-anything-2-hiera-large-image-torch`` +- Model source: https://ai.meta.com/sam2/ +- Model size: 148.68 MB +- Exposes embeddings? no +- Tags: ``segment-anything, torch, zero-shot`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("segment-anything-2-hiera-large-image-torch") + + # Segment inside boxes + dataset.apply_model( + model, + label_field="segmentations", + prompt_field="ground_truth", # can contain Detections or Keypoints + ) + + # Full automatic segmentations + dataset.apply_model(model, label_field="auto") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-segment-anything-2-hiera-large-video-torch: + +segment-anything-2-hiera-large-video-torch +__________________________________________ + +Segment Anything Model 2 (SAM2) from `SAM2: Segment Anything in Images and Videos `_. + +**Details** + +- Model name: ``segment-anything-2-hiera-large-video-torch`` +- Model source: https://ai.meta.com/sam2/ +- Model size: 148.68 MB +- Exposes embeddings? no +- Tags: ``segment-anything, torch, zero-shot, video`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + from fiftyone import ViewField as F + + dataset = foz.load_zoo_dataset("quickstart-video", max_samples=2) + + # Only retain detections in the first frame + ( + dataset + .match_frames(F("frame_number") > 1) + .set_field("frames.detections", None) + .save() + ) + + model = foz.load_zoo_model("segment-anything-2-hiera-large-video-torch") + + # Segment inside boxes and propagate to all frames + dataset.apply_model( + model, + label_field="segmentations", + prompt_field="frames.detections", # can contain Detections or Keypoints + ) + + session = fo.launch_app(dataset) + + + +.. _model-zoo-segment-anything-2-hiera-small-image-torch: + +segment-anything-2-hiera-small-image-torch +__________________________________________ + +Segment Anything Model 2 (SAM2) from `SAM2: Segment Anything in Images and Videos `_. + +**Details** + +- Model name: ``segment-anything-2-hiera-small-image-torch`` +- Model source: https://ai.meta.com/sam2/ +- Model size: 148.68 MB +- Exposes embeddings? no +- Tags: ``segment-anything, torch, zero-shot`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("segment-anything-2-hiera-small-image-torch") + + # Segment inside boxes + dataset.apply_model( + model, + label_field="segmentations", + prompt_field="ground_truth", # can contain Detections or Keypoints + ) + + # Full automatic segmentations + dataset.apply_model(model, label_field="auto") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-segment-anything-2-hiera-small-video-torch: + +segment-anything-2-hiera-small-video-torch +__________________________________________ + +Segment Anything Model 2 (SAM2) from `SAM2: Segment Anything in Images and Videos `_. + +**Details** + +- Model name: ``segment-anything-2-hiera-small-video-torch`` +- Model source: https://ai.meta.com/sam2/ +- Model size: 148.68 MB +- Exposes embeddings? no +- Tags: ``segment-anything, torch, zero-shot, video`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + from fiftyone import ViewField as F + + dataset = foz.load_zoo_dataset("quickstart-video", max_samples=2) + + # Only retain detections in the first frame + ( + dataset + .match_frames(F("frame_number") > 1) + .set_field("frames.detections", None) + .save() + ) + + model = foz.load_zoo_model("segment-anything-2-hiera-small-video-torch") + + # Segment inside boxes and propagate to all frames + dataset.apply_model( + model, + label_field="segmentations", + prompt_field="frames.detections", # can contain Detections or Keypoints + ) + + session = fo.launch_app(dataset) + + + +.. _model-zoo-segment-anything-2-hiera-tiny-image-torch: + +segment-anything-2-hiera-tiny-image-torch +_________________________________________ + +Segment Anything Model 2 (SAM2) from `SAM2: Segment Anything in Images and Videos `_. + +**Details** + +- Model name: ``segment-anything-2-hiera-tiny-image-torch`` +- Model source: https://ai.meta.com/sam2/ +- Model size: 148.68 MB +- Exposes embeddings? no +- Tags: ``segment-anything, torch, zero-shot`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("segment-anything-2-hiera-tiny-image-torch") + + # Segment inside boxes + dataset.apply_model( + model, + label_field="segmentations", + prompt_field="ground_truth", # can contain Detections or Keypoints + ) + + # Full automatic segmentations + dataset.apply_model(model, label_field="auto") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-segment-anything-2-hiera-tiny-video-torch: + +segment-anything-2-hiera-tiny-video-torch +_________________________________________ + +Segment Anything Model 2 (SAM2) from `SAM2: Segment Anything in Images and Videos `_. + +**Details** + +- Model name: ``segment-anything-2-hiera-tiny-video-torch`` +- Model source: https://ai.meta.com/sam2/ +- Model size: 148.68 MB +- Exposes embeddings? no +- Tags: ``segment-anything, torch, zero-shot, video`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + from fiftyone import ViewField as F + + dataset = foz.load_zoo_dataset("quickstart-video", max_samples=2) + + # Only retain detections in the first frame + ( + dataset + .match_frames(F("frame_number") > 1) + .set_field("frames.detections", None) + .save() + ) + + model = foz.load_zoo_model("segment-anything-2-hiera-tiny-video-torch") + + # Segment inside boxes and propagate to all frames + dataset.apply_model( + model, + label_field="segmentations", + prompt_field="frames.detections", # can contain Detections or Keypoints + ) + + session = fo.launch_app(dataset) + + + +.. _model-zoo-segment-anything-vitb-torch: + +segment-anything-vitb-torch +___________________________ + +Segment Anything Model (SAM) from `Segment Anything `_ with ViT-B/16 backbone trained on SA-1B. + +**Details** + +- Model name: ``segment-anything-vitb-torch`` +- Model source: https://segment-anything.com +- Model size: 715.34 KB +- Exposes embeddings? no +- Tags: ``segment-anything, sa-1b, torch, zero-shot`` + +**Requirements** + +- Packages: ``torch, torchvision, segment-anything`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("segment-anything-vitb-torch") + + # Segment inside boxes + dataset.apply_model( + model, + label_field="segmentations", + prompt_field="ground_truth", # can contain Detections or Keypoints + ) + + # Full automatic segmentations + dataset.apply_model(model, label_field="auto") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-segment-anything-vith-torch: + +segment-anything-vith-torch +___________________________ + +Segment Anything Model (SAM) from `Segment Anything `_ with ViT-H/16 backbone trained on SA-1B. + +**Details** + +- Model name: ``segment-anything-vith-torch`` +- Model source: https://segment-anything.com +- Model size: 4.78 MB +- Exposes embeddings? no +- Tags: ``segment-anything, sa-1b, torch, zero-shot`` + +**Requirements** + +- Packages: ``torch, torchvision, segment-anything`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("segment-anything-vith-torch") + + # Segment inside boxes + dataset.apply_model( + model, + label_field="segmentations", + prompt_field="ground_truth", # can contain Detections or Keypoints + ) + + # Full automatic segmentations + dataset.apply_model(model, label_field="auto") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-segment-anything-vitl-torch: + +segment-anything-vitl-torch +___________________________ + +Segment Anything Model (SAM) from `Segment Anything `_ with ViT-L/16 backbone trained on SA-1B. + +**Details** + +- Model name: ``segment-anything-vitl-torch`` +- Model source: https://segment-anything.com +- Model size: 2.33 MB +- Exposes embeddings? no +- Tags: ``segment-anything, sa-1b, torch, zero-shot`` + +**Requirements** + +- Packages: ``torch, torchvision, segment-anything`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("segment-anything-vitl-torch") + + # Segment inside boxes + dataset.apply_model( + model, + label_field="segmentations", + prompt_field="ground_truth", # can contain Detections or Keypoints + ) + + # Full automatic segmentations + dataset.apply_model(model, label_field="auto") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-segmentation-transformer-torch: + +segmentation-transformer-torch +______________________________ + +Hugging Face Transformers model for semantic segmentation. + +**Details** + +- Model name: ``segmentation-transformer-torch`` +- Model source: https://huggingface.co/docs/transformers/tasks/semantic_segmentation +- Exposes embeddings? no +- Tags: ``segmentation, torch, transformers`` + +**Requirements** + +- Packages: ``torch, torchvision, transformers`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("segmentation-transformer-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-shufflenetv2-0.5x-imagenet-torch: + +shufflenetv2-0.5x-imagenet-torch +________________________________ + +ShuffleNetV2 model from `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `_ with 0.5x output channels trained on ImageNet. + +**Details** + +- Model name: ``shufflenetv2-0.5x-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 5.28 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("shufflenetv2-0.5x-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-shufflenetv2-1.0x-imagenet-torch: + +shufflenetv2-1.0x-imagenet-torch +________________________________ + +ShuffleNetV2 model from `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design `_ with 1.0x output channels trained on ImageNet. + +**Details** + +- Model name: ``shufflenetv2-1.0x-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 8.79 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("shufflenetv2-1.0x-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-squeezenet-1.1-imagenet-torch: + +squeezenet-1.1-imagenet-torch +_____________________________ + +SqueezeNet 1.1 model from `the official SqueezeNet repo `_ trained on ImageNet. + +**Details** + +- Model name: ``squeezenet-1.1-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 4.74 MB +- Exposes embeddings? no +- Tags: ``classification, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("squeezenet-1.1-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-squeezenet-imagenet-torch: + +squeezenet-imagenet-torch +_________________________ + +SqueezeNet model from `SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size `_ trained on ImageNet. + +**Details** + +- Model name: ``squeezenet-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 4.79 MB +- Exposes embeddings? no +- Tags: ``classification, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("squeezenet-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-vgg11-bn-imagenet-torch: + +vgg11-bn-imagenet-torch +_______________________ + +VGG-11 model from `Very Deep Convolutional Networks for Large-Scale Image Recognition `_ with batch normalization trained on ImageNet. + +**Details** + +- Model name: ``vgg11-bn-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 506.88 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("vgg11-bn-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-vgg11-imagenet-torch: + +vgg11-imagenet-torch +____________________ + +VGG-11 model from `Very Deep Convolutional Networks for Large-Scale Image Recognition `_ trained on ImageNet. + +**Details** + +- Model name: ``vgg11-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 506.84 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("vgg11-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-vgg13-bn-imagenet-torch: + +vgg13-bn-imagenet-torch +_______________________ + +VGG-13 model from `Very Deep Convolutional Networks for Large-Scale Image Recognition `_ with batch normalization trained on ImageNet. + +**Details** + +- Model name: ``vgg13-bn-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 507.59 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("vgg13-bn-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-vgg13-imagenet-torch: + +vgg13-imagenet-torch +____________________ + +VGG-13 model from `Very Deep Convolutional Networks for Large-Scale Image Recognition `_ trained on ImageNet. + +**Details** + +- Model name: ``vgg13-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 507.54 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("vgg13-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-vgg16-bn-imagenet-torch: + +vgg16-bn-imagenet-torch +_______________________ + +VGG-16 model from `Very Deep Convolutional Networks for Large-Scale Image Recognition `_ with batch normalization trained on ImageNet. + +**Details** + +- Model name: ``vgg16-bn-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 527.87 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("vgg16-bn-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-vgg16-imagenet-torch: + +vgg16-imagenet-torch +____________________ + +VGG-16 model from `Very Deep Convolutional Networks for Large-Scale Image Recognition `_ trained on ImageNet. + +**Details** + +- Model name: ``vgg16-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 527.80 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("vgg16-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-vgg19-bn-imagenet-torch: + +vgg19-bn-imagenet-torch +_______________________ + +VGG-19 model from `Very Deep Convolutional Networks for Large-Scale Image Recognition `_ with batch normalization trained on ImageNet. + +**Details** + +- Model name: ``vgg19-bn-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 548.14 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("vgg19-bn-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-vgg19-imagenet-torch: + +vgg19-imagenet-torch +____________________ + +VGG-19 model from `Very Deep Convolutional Networks for Large-Scale Image Recognition `_ trained on ImageNet. + +**Details** + +- Model name: ``vgg19-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 548.05 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("vgg19-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-wide-resnet101-2-imagenet-torch: + +wide-resnet101-2-imagenet-torch +_______________________________ + +Wide ResNet-101-2 model from `Wide Residual Networks `_ trained on ImageNet. + +**Details** + +- Model name: ``wide-resnet101-2-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 242.90 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("wide-resnet101-2-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-wide-resnet50-2-imagenet-torch: + +wide-resnet50-2-imagenet-torch +______________________________ + +Wide ResNet-50-2 model from `Wide Residual Networks `_ trained on ImageNet. + +**Details** + +- Model name: ``wide-resnet50-2-imagenet-torch`` +- Model source: https://pytorch.org/vision/main/models.html +- Model size: 131.82 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, torch`` + +**Requirements** + +- Packages: ``torch, torchvision`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("wide-resnet50-2-imagenet-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolo-nas-torch: + +yolo-nas-torch +______________ + +YOLO-NAS is an open-source training library for advanced computer vision models. It specializes in accuracy and efficiency, supporting tasks like object detection. + +**Details** + +- Model name: ``yolo-nas-torch`` +- Model source: https://github.com/Deci-AI/super-gradients +- Exposes embeddings? no +- Tags: ``classification, torch, yolo`` + +**Requirements** + +- Packages: ``torch, torchvision, super-gradients`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolo-nas-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov10l-coco-torch: + +yolov10l-coco-torch +___________________ + +YOLOv10-L model trained on COCO. + +**Details** + +- Model name: ``yolov10l-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov10/ +- Model size: 50.00 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov10l-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov10m-coco-torch: + +yolov10m-coco-torch +___________________ + +YOLOv10-M model trained on COCO. + +**Details** + +- Model name: ``yolov10m-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov10/ +- Model size: 32.09 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov10m-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov10n-coco-torch: + +yolov10n-coco-torch +___________________ + +YOLOv10-N model trained on COCO. + +**Details** + +- Model name: ``yolov10n-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov10/ +- Model size: 5.59 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov10n-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov10s-coco-torch: + +yolov10s-coco-torch +___________________ + +YOLOv10-S model trained on COCO. + +**Details** + +- Model name: ``yolov10s-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov10/ +- Model size: 15.85 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov10s-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov10x-coco-torch: + +yolov10x-coco-torch +___________________ + +YOLOv10-X model trained on COCO. + +**Details** + +- Model name: ``yolov10x-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov10/ +- Model size: 61.41 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.2.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov10x-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov5l-coco-torch: + +yolov5l-coco-torch +__________________ + +Ultralytics YOLOv5l model trained on COCO. + +**Details** + +- Model name: ``yolov5l-coco-torch`` +- Model source: https://pytorch.org/hub/ultralytics_yolov5 +- Model size: 192.88 KB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov5l-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov5m-coco-torch: + +yolov5m-coco-torch +__________________ + +Ultralytics YOLOv5m model trained on COCO. + +**Details** + +- Model name: ``yolov5m-coco-torch`` +- Model source: https://pytorch.org/hub/ultralytics_yolov5 +- Model size: 81.91 KB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov5m-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov5n-coco-torch: + +yolov5n-coco-torch +__________________ + +Ultralytics YOLOv5n model trained on COCO. + +**Details** + +- Model name: ``yolov5n-coco-torch`` +- Model source: https://pytorch.org/hub/ultralytics_yolov5 +- Model size: 7.75 KB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov5n-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov5s-coco-torch: + +yolov5s-coco-torch +__________________ + +Ultralytics YOLOv5s model trained on COCO. + +**Details** + +- Model name: ``yolov5s-coco-torch`` +- Model source: https://pytorch.org/hub/ultralytics_yolov5 +- Model size: 28.25 KB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov5s-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov5x-coco-torch: + +yolov5x-coco-torch +__________________ + +Ultralytics YOLOv5x model trained on COCO. + +**Details** + +- Model name: ``yolov5x-coco-torch`` +- Model source: https://pytorch.org/hub/ultralytics_yolov5 +- Model size: 352.05 KB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov5x-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8l-coco-torch: + +yolov8l-coco-torch +__________________ + +Ultralytics YOLOv8l model trained on COCO. + +**Details** + +- Model name: ``yolov8l-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov8/ +- Model size: 83.70 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8l-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8l-obb-dotav1-torch: + +yolov8l-obb-dotav1-torch +________________________ + +YOLOv8l Oriented Bounding Box model. + +**Details** + +- Model name: ``yolov8l-obb-dotav1-torch`` +- Model source: https://docs.ultralytics.com/tasks/obb/ +- Model size: 85.36 MB +- Exposes embeddings? no +- Tags: ``detection, torch, yolo, polylines, obb`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8l-obb-dotav1-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8l-oiv7-torch: + +yolov8l-oiv7-torch +__________________ + +Ultralytics YOLOv8l model trained Open Images v7. + +**Details** + +- Model name: ``yolov8l-oiv7-torch`` +- Model source: https://docs.ultralytics.com/datasets/detect/open-images-v7 +- Model size: 83.70 MB +- Exposes embeddings? no +- Tags: ``detection, oiv7, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8l-oiv7-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8l-seg-coco-torch: + +yolov8l-seg-coco-torch +______________________ + +Ultralytics YOLOv8l Segmentation model trained on COCO. + +**Details** + +- Model name: ``yolov8l-seg-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov8/ +- Model size: 88.11 MB +- Exposes embeddings? no +- Tags: ``segmentation, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8l-seg-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8l-world-torch: + +yolov8l-world-torch +___________________ + +YOLOv8l-World model. + +**Details** + +- Model name: ``yolov8l-world-torch`` +- Model source: https://docs.ultralytics.com/models/yolo-world/ +- Model size: 91.23 MB +- Exposes embeddings? no +- Tags: ``detection, torch, yolo, zero-shot`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8l-world-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8m-coco-torch: + +yolov8m-coco-torch +__________________ + +Ultralytics YOLOv8m model trained on COCO. + +**Details** + +- Model name: ``yolov8m-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov8/ +- Model size: 49.70 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8m-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8m-obb-dotav1-torch: + +yolov8m-obb-dotav1-torch +________________________ + +YOLOv8m Oriented Bounding Box model. + +**Details** + +- Model name: ``yolov8m-obb-dotav1-torch`` +- Model source: https://docs.ultralytics.com/tasks/obb/ +- Model size: 50.84 MB +- Exposes embeddings? no +- Tags: ``detection, torch, yolo, polylines, obb`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8m-obb-dotav1-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8m-oiv7-torch: + +yolov8m-oiv7-torch +__________________ + +Ultralytics YOLOv8m model trained Open Images v7. + +**Details** + +- Model name: ``yolov8m-oiv7-torch`` +- Model source: https://docs.ultralytics.com/datasets/detect/open-images-v7 +- Model size: 49.70 MB +- Exposes embeddings? no +- Tags: ``detection, oiv7, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8m-oiv7-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8m-seg-coco-torch: + +yolov8m-seg-coco-torch +______________________ + +Ultralytics YOLOv8m Segmentation model trained on COCO. + +**Details** + +- Model name: ``yolov8m-seg-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov8/ +- Model size: 52.36 MB +- Exposes embeddings? no +- Tags: ``segmentation, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8m-seg-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8m-world-torch: + +yolov8m-world-torch +___________________ + +YOLOv8m-World model. + +**Details** + +- Model name: ``yolov8m-world-torch`` +- Model source: https://docs.ultralytics.com/models/yolo-world/ +- Model size: 55.89 MB +- Exposes embeddings? no +- Tags: ``detection, torch, yolo, zero-shot`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8m-world-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8n-coco-torch: + +yolov8n-coco-torch +__________________ + +Ultralytics YOLOv8n model trained on COCO. + +**Details** + +- Model name: ``yolov8n-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov8/ +- Model size: 6.23 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8n-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8n-obb-dotav1-torch: + +yolov8n-obb-dotav1-torch +________________________ + +YOLOv8n Oriented Bounding Box model. + +**Details** + +- Model name: ``yolov8n-obb-dotav1-torch`` +- Model source: https://docs.ultralytics.com/tasks/obb/ +- Model size: 6.24 MB +- Exposes embeddings? no +- Tags: ``detection, torch, yolo, polylines, obb`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8n-obb-dotav1-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8n-oiv7-torch: + +yolov8n-oiv7-torch +__________________ + +Ultralytics YOLOv8n model trained on Open Images v7. + +**Details** + +- Model name: ``yolov8n-oiv7-torch`` +- Model source: https://docs.ultralytics.com/datasets/detect/open-images-v7 +- Model size: 6.23 MB +- Exposes embeddings? no +- Tags: ``detection, oiv7, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8n-oiv7-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8n-seg-coco-torch: + +yolov8n-seg-coco-torch +______________________ + +Ultralytics YOLOv8n Segmentation model trained on COCO. + +**Details** + +- Model name: ``yolov8n-seg-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov8/ +- Model size: 6.73 MB +- Exposes embeddings? no +- Tags: ``segmentation, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8n-seg-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8s-coco-torch: + +yolov8s-coco-torch +__________________ + +Ultralytics YOLOv8s model trained on COCO. + +**Details** + +- Model name: ``yolov8s-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov8/ +- Model size: 21.53 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8s-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8s-obb-dotav1-torch: + +yolov8s-obb-dotav1-torch +________________________ + +YOLOv8s Oriented Bounding Box model. + +**Details** + +- Model name: ``yolov8s-obb-dotav1-torch`` +- Model source: https://docs.ultralytics.com/tasks/obb/ +- Model size: 22.17 MB +- Exposes embeddings? no +- Tags: ``detection, torch, yolo, polylines, obb`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8s-obb-dotav1-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8s-oiv7-torch: + +yolov8s-oiv7-torch +__________________ + +Ultralytics YOLOv8s model trained on Open Images v7. + +**Details** + +- Model name: ``yolov8s-oiv7-torch`` +- Model source: https://docs.ultralytics.com/datasets/detect/open-images-v7 +- Model size: 21.53 MB +- Exposes embeddings? no +- Tags: ``detection, oiv7, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8s-oiv7-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8s-seg-coco-torch: + +yolov8s-seg-coco-torch +______________________ + +Ultralytics YOLOv8s Segmentation model trained on COCO. + +**Details** + +- Model name: ``yolov8s-seg-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov8/ +- Model size: 22.79 MB +- Exposes embeddings? no +- Tags: ``segmentation, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8s-seg-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8s-world-torch: + +yolov8s-world-torch +___________________ + +YOLOv8s-World model. + +**Details** + +- Model name: ``yolov8s-world-torch`` +- Model source: https://docs.ultralytics.com/models/yolo-world/ +- Model size: 25.91 MB +- Exposes embeddings? no +- Tags: ``detection, torch, yolo, zero-shot`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8s-world-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8x-coco-torch: + +yolov8x-coco-torch +__________________ + +Ultralytics YOLOv8x model trained on COCO. + +**Details** + +- Model name: ``yolov8x-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov8/ +- Model size: 130.53 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8x-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8x-obb-dotav1-torch: + +yolov8x-obb-dotav1-torch +________________________ + +YOLOv8x Oriented Bounding Box model. + +**Details** + +- Model name: ``yolov8x-obb-dotav1-torch`` +- Model source: https://docs.ultralytics.com/tasks/obb/ +- Model size: 133.07 MB +- Exposes embeddings? no +- Tags: ``detection, torch, yolo, polylines, obb`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8x-obb-dotav1-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8x-oiv7-torch: + +yolov8x-oiv7-torch +__________________ + +Ultralytics YOLOv8x model trained Open Images v7. + +**Details** + +- Model name: ``yolov8x-oiv7-torch`` +- Model source: https://docs.ultralytics.com/datasets/detect/open-images-v7 +- Model size: 130.53 MB +- Exposes embeddings? no +- Tags: ``detection, oiv7, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8x-oiv7-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8x-seg-coco-torch: + +yolov8x-seg-coco-torch +______________________ + +Ultralytics YOLOv8x Segmentation model trained on COCO. + +**Details** + +- Model name: ``yolov8x-seg-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov8/ +- Model size: 137.40 MB +- Exposes embeddings? no +- Tags: ``segmentation, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8x-seg-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov8x-world-torch: + +yolov8x-world-torch +___________________ + +YOLOv8x-World model. + +**Details** + +- Model name: ``yolov8x-world-torch`` +- Model source: https://docs.ultralytics.com/models/yolo-world/ +- Model size: 141.11 MB +- Exposes embeddings? no +- Tags: ``detection, torch, yolo, zero-shot`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov8x-world-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov9c-coco-torch: + +yolov9c-coco-torch +__________________ + +YOLOv9-C model trained on COCO. + +**Details** + +- Model name: ``yolov9c-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov9/ +- Model size: 49.40 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov9c-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov9c-seg-coco-torch: + +yolov9c-seg-coco-torch +______________________ + +YOLOv9-C Segmentation model trained on COCO. + +**Details** + +- Model name: ``yolov9c-seg-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov9/#__tabbed_1_2 +- Model size: 107.20 MB +- Exposes embeddings? no +- Tags: ``segmentation, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.42`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov9c-seg-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov9e-coco-torch: + +yolov9e-coco-torch +__________________ + +YOLOv9-E model trained on COCO. + +**Details** + +- Model name: ``yolov9e-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov9/ +- Model size: 112.09 MB +- Exposes embeddings? no +- Tags: ``detection, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.0`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov9e-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolov9e-seg-coco-torch: + +yolov9e-seg-coco-torch +______________________ + +YOLOv9-E Segmentation model trained on COCO. + +**Details** + +- Model name: ``yolov9e-seg-coco-torch`` +- Model source: https://docs.ultralytics.com/models/yolov9/#__tabbed_1_2 +- Model size: 232.20 MB +- Exposes embeddings? no +- Tags: ``segmentation, coco, torch, yolo`` + +**Requirements** + +- Packages: ``torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.1.42`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolov9e-seg-coco-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-zero-shot-classification-transformer-torch: + +zero-shot-classification-transformer-torch +__________________________________________ + +Hugging Face Transformers model for zero-shot image classification. + +**Details** + +- Model name: ``zero-shot-classification-transformer-torch`` +- Model source: https://huggingface.co/docs/transformers/tasks/zero_shot_image_classification +- Exposes embeddings? yes +- Tags: ``classification, logits, embeddings, torch, transformers, zero-shot`` + +**Requirements** + +- Packages: ``torch, torchvision, transformers`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("zero-shot-classification-transformer-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-zero-shot-detection-transformer-torch: + +zero-shot-detection-transformer-torch +_____________________________________ + +Hugging Face Transformers model for zero-shot object detection. + +**Details** + +- Model name: ``zero-shot-detection-transformer-torch`` +- Model source: https://huggingface.co/docs/transformers/tasks/zero_shot_object_detection +- Exposes embeddings? yes +- Tags: ``detection, logits, embeddings, torch, transformers, zero-shot`` + +**Requirements** + +- Packages: ``torch, torchvision, transformers`` + +- CPU support + + - yes + +- GPU support + + - yes + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("zero-shot-detection-transformer-torch") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-tensorflow-models: + +TensorFlow models +----------------- + +.. _model-zoo-centernet-hg104-1024-coco-tf2: + +centernet-hg104-1024-coco-tf2 +_____________________________ + +CenterNet model from `Objects as Points `_ with the Hourglass-104 backbone trained on COCO resized to 1024x1024. + +**Details** + +- Model name: ``centernet-hg104-1024-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 1.33 GB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("centernet-hg104-1024-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-centernet-hg104-512-coco-tf2: + +centernet-hg104-512-coco-tf2 +____________________________ + +CenterNet model from `Objects as Points `_ with the Hourglass-104 backbone trained on COCO resized to 512x512. + +**Details** + +- Model name: ``centernet-hg104-512-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 1.49 GB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("centernet-hg104-512-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-centernet-mobilenet-v2-fpn-512-coco-tf2: + +centernet-mobilenet-v2-fpn-512-coco-tf2 +_______________________________________ + +CenterNet model from `Objects as Points `_ with the MobileNetV2 backbone trained on COCO resized to 512x512. + +**Details** + +- Model name: ``centernet-mobilenet-v2-fpn-512-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 41.98 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("centernet-mobilenet-v2-fpn-512-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-centernet-resnet101-v1-fpn-512-coco-tf2: + +centernet-resnet101-v1-fpn-512-coco-tf2 +_______________________________________ + +CenterNet model from `Objects as Points `_ with the ResNet-101v1 backbone + FPN trained on COCO resized to 512x512. + +**Details** + +- Model name: ``centernet-resnet101-v1-fpn-512-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 329.96 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("centernet-resnet101-v1-fpn-512-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-centernet-resnet50-v1-fpn-512-coco-tf2: + +centernet-resnet50-v1-fpn-512-coco-tf2 +______________________________________ + +CenterNet model from `Objects as Points `_ with the ResNet-50-v1 backbone + FPN trained on COCO resized to 512x512. + +**Details** + +- Model name: ``centernet-resnet50-v1-fpn-512-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 194.61 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("centernet-resnet50-v1-fpn-512-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-centernet-resnet50-v2-512-coco-tf2: + +centernet-resnet50-v2-512-coco-tf2 +__________________________________ + +CenterNet model from `Objects as Points `_ with the ResNet-50v2 backbone trained on COCO resized to 512x512. + +**Details** + +- Model name: ``centernet-resnet50-v2-512-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 226.95 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("centernet-resnet50-v2-512-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-deeplabv3-cityscapes-tf: + +deeplabv3-cityscapes-tf +_______________________ + +DeepLabv3+ semantic segmentation model from `Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation `_ with Xception backbone trained on the Cityscapes dataset. + +**Details** + +- Model name: ``deeplabv3-cityscapes-tf`` +- Model source: https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md +- Model size: 158.04 MB +- Exposes embeddings? no +- Tags: ``segmentation, cityscapes, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("deeplabv3-cityscapes-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-deeplabv3-mnv2-cityscapes-tf: + +deeplabv3-mnv2-cityscapes-tf +____________________________ + +DeepLabv3+ semantic segmentation model from `Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation `_ with MobileNetV2 backbone trained on the Cityscapes dataset. + +**Details** + +- Model name: ``deeplabv3-mnv2-cityscapes-tf`` +- Model source: https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md +- Model size: 8.37 MB +- Exposes embeddings? no +- Tags: ``segmentation, cityscapes, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("deeplabv3-mnv2-cityscapes-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d0-512-coco-tf2: + +efficientdet-d0-512-coco-tf2 +____________________________ + +EfficientDet-D0 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO resized to 512x512. + +**Details** + +- Model name: ``efficientdet-d0-512-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 29.31 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d0-512-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d0-coco-tf1: + +efficientdet-d0-coco-tf1 +________________________ + +EfficientDet-D0 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO. + +**Details** + +- Model name: ``efficientdet-d0-coco-tf1`` +- Model source: https://github.com/voxel51/automl/tree/master/efficientdet +- Model size: 38.20 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=1.14,<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=1.14,<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d0-coco-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d1-640-coco-tf2: + +efficientdet-d1-640-coco-tf2 +____________________________ + +EfficientDet-D1 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO resized to 640x640. + +**Details** + +- Model name: ``efficientdet-d1-640-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 49.44 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d1-640-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d1-coco-tf1: + +efficientdet-d1-coco-tf1 +________________________ + +EfficientDet-D1 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO. + +**Details** + +- Model name: ``efficientdet-d1-coco-tf1`` +- Model source: https://github.com/voxel51/automl/tree/master/efficientdet +- Model size: 61.64 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=1.14,<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=1.14,<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d1-coco-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d2-768-coco-tf2: + +efficientdet-d2-768-coco-tf2 +____________________________ + +EfficientDet-D2 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO resized to 768x768. + +**Details** + +- Model name: ``efficientdet-d2-768-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 60.01 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d2-768-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d2-coco-tf1: + +efficientdet-d2-coco-tf1 +________________________ + +EfficientDet-D2 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO. + +**Details** + +- Model name: ``efficientdet-d2-coco-tf1`` +- Model source: https://github.com/voxel51/automl/tree/master/efficientdet +- Model size: 74.00 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=1.14,<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=1.14,<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d2-coco-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d3-896-coco-tf2: + +efficientdet-d3-896-coco-tf2 +____________________________ + +EfficientDet-D3 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO resized to 896x896. + +**Details** + +- Model name: ``efficientdet-d3-896-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 88.56 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d3-896-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d3-coco-tf1: + +efficientdet-d3-coco-tf1 +________________________ + +EfficientDet-D3 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO. + +**Details** + +- Model name: ``efficientdet-d3-coco-tf1`` +- Model source: https://github.com/voxel51/automl/tree/master/efficientdet +- Model size: 106.44 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=1.14,<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=1.14,<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d3-coco-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d4-1024-coco-tf2: + +efficientdet-d4-1024-coco-tf2 +_____________________________ + +EfficientDet-D4 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO resized to 1024x1024. + +**Details** + +- Model name: ``efficientdet-d4-1024-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 151.15 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d4-1024-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d4-coco-tf1: + +efficientdet-d4-coco-tf1 +________________________ + +EfficientDet-D4 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO. + +**Details** + +- Model name: ``efficientdet-d4-coco-tf1`` +- Model source: https://github.com/voxel51/automl/tree/master/efficientdet +- Model size: 175.33 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=1.14,<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=1.14,<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d4-coco-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d5-1280-coco-tf2: + +efficientdet-d5-1280-coco-tf2 +_____________________________ + +EfficientDet-D5 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO resized to 1280x1280. + +**Details** + +- Model name: ``efficientdet-d5-1280-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 244.41 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d5-1280-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d5-coco-tf1: + +efficientdet-d5-coco-tf1 +________________________ + +EfficientDet-D5 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO. + +**Details** + +- Model name: ``efficientdet-d5-coco-tf1`` +- Model source: https://github.com/voxel51/automl/tree/master/efficientdet +- Model size: 275.81 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=1.14,<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=1.14,<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d5-coco-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d6-1280-coco-tf2: + +efficientdet-d6-1280-coco-tf2 +_____________________________ + +EfficientDet-D6 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO resized to 1280x1280. + +**Details** + +- Model name: ``efficientdet-d6-1280-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 375.63 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d6-1280-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d6-coco-tf1: + +efficientdet-d6-coco-tf1 +________________________ + +EfficientDet-D6 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO. + +**Details** + +- Model name: ``efficientdet-d6-coco-tf1`` +- Model source: https://github.com/voxel51/automl/tree/master/efficientdet +- Model size: 416.43 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=1.14,<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=1.14,<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d6-coco-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-efficientdet-d7-1536-coco-tf2: + +efficientdet-d7-1536-coco-tf2 +_____________________________ + +EfficientDet-D7 model from `EfficientDet: Scalable and Efficient Object Detection `_ trained on COCO resized to 1536x1536. + +**Details** + +- Model name: ``efficientdet-d7-1536-coco-tf2`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 376.20 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("efficientdet-d7-1536-coco-tf2") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-faster-rcnn-inception-resnet-atrous-v2-coco-tf: + +faster-rcnn-inception-resnet-atrous-v2-coco-tf +______________________________________________ + +Faster R-CNN model from `Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks `_ atrous version with Inception backbone trained on COCO. + +**Details** + +- Model name: ``faster-rcnn-inception-resnet-atrous-v2-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 234.46 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("faster-rcnn-inception-resnet-atrous-v2-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-faster-rcnn-inception-resnet-atrous-v2-lowproposals-coco-tf: + +faster-rcnn-inception-resnet-atrous-v2-lowproposals-coco-tf +___________________________________________________________ + +Faster R-CNN model from `Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks `_ atrous version with low-proposals and Inception backbone trained on COCO. + +**Details** + +- Model name: ``faster-rcnn-inception-resnet-atrous-v2-lowproposals-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 234.46 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("faster-rcnn-inception-resnet-atrous-v2-lowproposals-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-faster-rcnn-inception-v2-coco-tf: + +faster-rcnn-inception-v2-coco-tf +________________________________ + +Faster R-CNN model from `Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks `_ with Inception v2 backbone trained on COCO. + +**Details** + +- Model name: ``faster-rcnn-inception-v2-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 52.97 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("faster-rcnn-inception-v2-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-faster-rcnn-nas-coco-tf: + +faster-rcnn-nas-coco-tf +_______________________ + +Faster R-CNN model from `Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks `_ with NAS-net backbone trained on COCO. + +**Details** + +- Model name: ``faster-rcnn-nas-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 404.95 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("faster-rcnn-nas-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-faster-rcnn-nas-lowproposals-coco-tf: + +faster-rcnn-nas-lowproposals-coco-tf +____________________________________ + +Faster R-CNN model from `Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks `_ with low-proposals and NAS-net backbone trained on COCO. + +**Details** + +- Model name: ``faster-rcnn-nas-lowproposals-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 404.88 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("faster-rcnn-nas-lowproposals-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-faster-rcnn-resnet101-coco-tf: + +faster-rcnn-resnet101-coco-tf +_____________________________ + +Faster R-CNN model from `Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks `_ with ResNet-101 backbone trained on COCO. + +**Details** + +- Model name: ``faster-rcnn-resnet101-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 186.41 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("faster-rcnn-resnet101-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-faster-rcnn-resnet101-lowproposals-coco-tf: + +faster-rcnn-resnet101-lowproposals-coco-tf +__________________________________________ + +Faster R-CNN model from `Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks `_ with low-proposals and ResNet-101 backbone trained on COCO. + +**Details** + +- Model name: ``faster-rcnn-resnet101-lowproposals-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 186.41 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("faster-rcnn-resnet101-lowproposals-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-faster-rcnn-resnet50-coco-tf: + +faster-rcnn-resnet50-coco-tf +____________________________ + +Faster R-CNN model from `Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks `_ with ResNet-50 backbone trained on COCO. + +**Details** + +- Model name: ``faster-rcnn-resnet50-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 113.57 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("faster-rcnn-resnet50-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-faster-rcnn-resnet50-lowproposals-coco-tf: + +faster-rcnn-resnet50-lowproposals-coco-tf +_________________________________________ + +Faster R-CNN model from `Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks `_ with low-proposals and ResNet-50 backbone trained on COCO. + +**Details** + +- Model name: ``faster-rcnn-resnet50-lowproposals-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 113.57 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("faster-rcnn-resnet50-lowproposals-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-inception-resnet-v2-imagenet-tf1: + +inception-resnet-v2-imagenet-tf1 +________________________________ + +Inception v2 model from `Rethinking the Inception Architecture for Computer Vision `_ trained on ImageNet. + +**Details** + +- Model name: ``inception-resnet-v2-imagenet-tf1`` +- Model source: https://github.com/tensorflow/models/tree/archive/research/slim#pre-trained-models +- Model size: 213.81 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("inception-resnet-v2-imagenet-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-inception-v4-imagenet-tf1: + +inception-v4-imagenet-tf1 +_________________________ + +Inception v4 model from `Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning `_ trained on ImageNet. + +**Details** + +- Model name: ``inception-v4-imagenet-tf1`` +- Model source: https://github.com/tensorflow/models/tree/archive/research/slim#pre-trained-models +- Model size: 163.31 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("inception-v4-imagenet-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-mask-rcnn-inception-resnet-v2-atrous-coco-tf: + +mask-rcnn-inception-resnet-v2-atrous-coco-tf +____________________________________________ + +Mask R-CNN model from `Mask R-CNN `_ atrous version with Inception backbone trained on COCO. + +**Details** + +- Model name: ``mask-rcnn-inception-resnet-v2-atrous-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 254.51 MB +- Exposes embeddings? no +- Tags: ``instances, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("mask-rcnn-inception-resnet-v2-atrous-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-mask-rcnn-inception-v2-coco-tf: + +mask-rcnn-inception-v2-coco-tf +______________________________ + +Mask R-CNN model from `Mask R-CNN `_ with Inception backbone trained on COCO. + +**Details** + +- Model name: ``mask-rcnn-inception-v2-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 64.03 MB +- Exposes embeddings? no +- Tags: ``instances, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("mask-rcnn-inception-v2-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-mask-rcnn-resnet101-atrous-coco-tf: + +mask-rcnn-resnet101-atrous-coco-tf +__________________________________ + +Mask R-CNN model from `Mask R-CNN `_ atrous version with ResNet-101 backbone trained on COCO. + +**Details** + +- Model name: ``mask-rcnn-resnet101-atrous-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 211.56 MB +- Exposes embeddings? no +- Tags: ``instances, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("mask-rcnn-resnet101-atrous-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-mask-rcnn-resnet50-atrous-coco-tf: + +mask-rcnn-resnet50-atrous-coco-tf +_________________________________ + +Mask R-CNN model from `Mask R-CNN `_ atrous version with ResNet-50 backbone trained on COCO. + +**Details** + +- Model name: ``mask-rcnn-resnet50-atrous-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 138.29 MB +- Exposes embeddings? no +- Tags: ``instances, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("mask-rcnn-resnet50-atrous-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-mobilenet-v2-imagenet-tf1: + +mobilenet-v2-imagenet-tf1 +_________________________ + +MobileNetV2 model from `MobileNetV2: Inverted Residuals and Linear Bottlenecks `_ trained on ImageNet. + +**Details** + +- Model name: ``mobilenet-v2-imagenet-tf1`` +- Model source: None +- Model size: 13.64 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("mobilenet-v2-imagenet-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-resnet-v1-50-imagenet-tf1: + +resnet-v1-50-imagenet-tf1 +_________________________ + +ResNet-50 v1 model from `Deep Residual Learning for Image Recognition `_ trained on ImageNet. + +**Details** + +- Model name: ``resnet-v1-50-imagenet-tf1`` +- Model source: https://github.com/tensorflow/models/tree/archive/research/slim#pre-trained-models +- Model size: 97.84 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("resnet-v1-50-imagenet-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-resnet-v2-50-imagenet-tf1: + +resnet-v2-50-imagenet-tf1 +_________________________ + +ResNet-50 v2 model from `Deep Residual Learning for Image Recognition `_ trained on ImageNet. + +**Details** + +- Model name: ``resnet-v2-50-imagenet-tf1`` +- Model source: https://github.com/tensorflow/models/tree/archive/research/slim#pre-trained-models +- Model size: 97.86 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("resnet-v2-50-imagenet-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-rfcn-resnet101-coco-tf: + +rfcn-resnet101-coco-tf +______________________ + +R-FCN object detection model from `R-FCN: Object Detection via Region-based Fully Convolutional Networks `_ with ResNet-101 backbone trained on COCO. + +**Details** + +- Model name: ``rfcn-resnet101-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 208.16 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("rfcn-resnet101-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-ssd-inception-v2-coco-tf: + +ssd-inception-v2-coco-tf +________________________ + +Inception Single Shot Detector model from `SSD: Single Shot MultiBox Detector `_ trained on COCO. + +**Details** + +- Model name: ``ssd-inception-v2-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 97.50 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("ssd-inception-v2-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-ssd-mobilenet-v1-coco-tf: + +ssd-mobilenet-v1-coco-tf +________________________ + +Single Shot Detector model from `SSD: Single Shot MultiBox Detector `_ with MobileNetV1 backbone trained on COCO. + +**Details** + +- Model name: ``ssd-mobilenet-v1-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 27.83 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("ssd-mobilenet-v1-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-ssd-mobilenet-v1-fpn-640-coco17: + +ssd-mobilenet-v1-fpn-640-coco17 +_______________________________ + +MobileNetV1 model from `MobileNetV2: Inverted Residuals and Linear Bottlenecks `_ resized to 640x640. + +**Details** + +- Model name: ``ssd-mobilenet-v1-fpn-640-coco17`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 43.91 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("ssd-mobilenet-v1-fpn-640-coco17") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-ssd-mobilenet-v1-fpn-coco-tf: + +ssd-mobilenet-v1-fpn-coco-tf +____________________________ + +FPN Single Shot Detector model from `SSD: Single Shot MultiBox Detector `_ with MobileNetV1 backbone trained on COCO. + +**Details** + +- Model name: ``ssd-mobilenet-v1-fpn-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 48.97 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("ssd-mobilenet-v1-fpn-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-ssd-mobilenet-v2-320-coco17: + +ssd-mobilenet-v2-320-coco17 +___________________________ + +MobileNetV2 model from `MobileNetV2: Inverted Residuals and Linear Bottlenecks `_ resized to 320x320. + +**Details** + +- Model name: ``ssd-mobilenet-v2-320-coco17`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf2_detection_zoo.md +- Model size: 43.91 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf2`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow>=2|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu>=2|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("ssd-mobilenet-v2-320-coco17") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-ssd-resnet50-fpn-coco-tf: + +ssd-resnet50-fpn-coco-tf +________________________ + +FPN Single Shot Detector model from `SSD: Single Shot MultiBox Detector `_ with ResNet-50 backbone trained on COCO. + +**Details** + +- Model name: ``ssd-resnet50-fpn-coco-tf`` +- Model source: https://github.com/tensorflow/models/blob/archive/research/object_detection/g3doc/tf1_detection_zoo.md +- Model size: 128.07 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow|tensorflow-macos`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu|tensorflow>=2|tensorflow-macos`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("ssd-resnet50-fpn-coco-tf") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-vgg16-imagenet-tf1: + +vgg16-imagenet-tf1 +__________________ + +VGG-16 model from `Very Deep Convolutional Networks for Large-Scale Image Recognition `_ trained on ImageNet. + +**Details** + +- Model name: ``vgg16-imagenet-tf1`` +- Model source: https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md +- Model size: 527.80 MB +- Exposes embeddings? yes +- Tags: ``classification, embeddings, logits, imagenet, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "imagenet-sample", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("vgg16-imagenet-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + + + +.. _model-zoo-yolo-v2-coco-tf1: + +yolo-v2-coco-tf1 +________________ + +YOLOv2 model from `YOLO9000: Better, Faster, Stronger `_ trained on COCO. + +**Details** + +- Model name: ``yolo-v2-coco-tf1`` +- Model source: https://github.com/thtrieu/darkflow +- Model size: 194.49 MB +- Exposes embeddings? no +- Tags: ``detection, coco, tf1`` + +**Requirements** + +- CPU support + + - yes + - Packages: ``tensorflow<2`` + +- GPU support + + - yes + - Packages: ``tensorflow-gpu<2`` + +**Example usage** + +.. code-block:: python + :linenos: + + import fiftyone as fo + import fiftyone.zoo as foz + + dataset = foz.load_zoo_dataset( + "coco-2017", + split="validation", + dataset_name=fo.get_default_dataset_name(), + max_samples=50, + shuffle=True, + ) + + model = foz.load_zoo_model("yolo-v2-coco-tf1") + + dataset.apply_model(model, label_field="predictions") + + session = fo.launch_app(dataset) + diff --git a/docs/source/user_guide/model_zoo/remote.rst b/docs/source/model_zoo/remote.rst similarity index 100% rename from docs/source/user_guide/model_zoo/remote.rst rename to docs/source/model_zoo/remote.rst diff --git a/docs/source/redirects b/docs/source/redirects index dc993b27d6..f06f309257 100644 --- a/docs/source/redirects +++ b/docs/source/redirects @@ -6,6 +6,15 @@ # from/broken/page2.ext to/existing/page2.ext # +# Introduced in v1.0.0 +user_guide/brain.rst brain.rst +user_guide/dataset_zoo/index.rst dataset_zoo/index.rst +user_guide/dataset_zoo/datasets.rst dataset_zoo/datasets.rst +user_guide/dataset_zoo/api.rst dataset_zoo/api.rst +user_guide/model_zoo/index.rst model_zoo/index.rst +user_guide/model_zoo/models.rst model_zoo/models.rst +user_guide/model_zoo/api.rst model_zoo/api.rst + # Introduced in v0.7.4 tutorials/label_mistakes.html tutorials/classification_mistakes.html recipes/detection_mistakenness.html tutorials/detection_mistakes.html diff --git a/docs/source/user_guide/index.rst b/docs/source/user_guide/index.rst index 037fd751a0..6482dfe9e3 100644 --- a/docs/source/user_guide/index.rst +++ b/docs/source/user_guide/index.rst @@ -86,24 +86,6 @@ on your data quickly and easily. :button_text: Learn more about drawing labels :button_link: draw_labels.html -.. customcalloutitem:: - :header: FiftyOne Dataset Zoo - :description: A collection of popular datasets that you can load into FiftyOne with a single command. - :button_text: Check out the Dataset Zoo - :button_link: dataset_zoo/index.html - -.. customcalloutitem:: - :header: FiftyOne Model Zoo - :description: A collection of popular models that you can apply to your FiftyOne datasets. - :button_text: Check out the Model Zoo - :button_link: model_zoo/index.html - -.. customcalloutitem:: - :header: FiftyOne Brain - :description: Use the FiftyOne Brain to automatically get insights into your datasets. - :button_text: Learn more about the FiftyOne Brain - :button_link: brain.html - .. customcalloutitem:: :header: Configuring FiftyOne :description: Customize the default behavior of the FiftyOne library to suit your needs. @@ -135,7 +117,4 @@ on your data quickly and easily. Evaluating models Exporting datasets Drawing labels on samples - FiftyOne Dataset Zoo - FiftyOne Model Zoo - FiftyOne Brain Configuring FiftyOne