PixelLib

Pixellib is a library for performing segmentation of images and videos. It supports the two major types of image segmentation:

1.Semantic segmentation

2.Instance segmentation

You can implement both semantic and instance segmentation with few lines of code.

There are two types of Deeplabv3+ models available for performing semantic segmentation with PixelLib:

1. Deeplabv3+ model with xception as network backbone trained on Ade20k dataset, a dataset with 150 classes of objects.
2. Deeplabv3+ model with xception as network backbone trained on Pascalvoc dataset, a dataset with 20 classes of objects.

Instance segmentation is implemented with PixelLib by using Mask R-CNN model trained on coco dataset.

Note Deeplab and mask r-ccn models are available in the release of this repository.

Install Tensorflow:

Install latest version of tensorflow(Tensorflow 2.0+) with:

pip3 install tensorflow

Install Pixellib with:

pip3 install pixellib --upgrade

Visit PixelLib's official documentation on readthedocs

Read the following tutorials on performing both semantic and instance segmentation of images and videos with PixelLib.

Tutorial On Semantic Segmentation of Images With PixelLib Using Ade20k model

Tutorial On Semantic Segmentation of Videos With PixelLib Using Ade20k model

Tutorial On Semantic Segmentation of Images With PixelLib Using Pascalvoc model

Tutorial On Semantic Segmentation of Videos With PixelLib Using Ade20k model

Tutorial On Instance Segmentation of Images With PixelLib Using Mask-RCNN

Tutorial On Instance Segmentation of Videos With PixelLib Using Mask-RCNN

References

1. Bonlime, Keras implementation of Deeplab v3+ with pretrained weights https://github.com/bonlime/keras-deeplab-v3-plus

2. Liang-Chieh Chen. et al, Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation https://arxiv.org/abs/1802.02611

3. Matterport, Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow https://github.com/matterport/Mask_RCNN

4. Kaiming He et al, Mask R-CNN https://arxiv.org/abs/1703.06870

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