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356 changes: 226 additions & 130 deletions README.md
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1 change: 1 addition & 0 deletions docs/compression_algorithms/Quantization.md
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Expand Up @@ -136,6 +136,7 @@ This regime is used when `"target_device": "CPU"` or `"target_device": "ANY"` se

---

<a name="mixed_precision_quantization"></a>
#### Mixed-Precision Quantization

Quantization to lower precisions (e.g. 6, 4, 2 bits) is an efficient way to accelerate inference of neural networks.
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42 changes: 25 additions & 17 deletions examples/tensorflow/classification/README.md
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Expand Up @@ -9,6 +9,14 @@ This sample demonstrates a DL model compression in case of the Image Classificat
- Export to Frozen Graph or TensorFlow SavedModel that is supported by the OpenVINO™ toolkit.
- Distributed training on multiple GPUs on one machine is supported using [tf.distribute.MirroredStrategy](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy).

## Installation

To work with the sample you should install the corresponding Python package dependencies

```
pip install -r examples/tensorflow/requirements.txt
```

## Quantize Pretrained Model

This scenario demonstrates quantization with fine-tuning of MobileNetV2 on the ImageNet dataset.
Expand Down Expand Up @@ -102,30 +110,30 @@ To export a model to the OpenVINO IR and run it using the Intel® Deep Learning

|**Model**|**Compression algorithm**|**Dataset**|**Accuracy (Drop) %**|**NNCF config file**|**TensorFlow checkpoint**|
| :---: | :---: | :---: | :---: | :---: | :---: |
|Inception V3|INT8 w:sym,per-tensor a:sym,per-tensor |ImageNet|78.35 (-0.45)|[inception_v3_imagenet_int8.json](configs/quantization/inception_v3_imagenet_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/inception_v3_int8_w_sym_t_half_a_sym_t.tar.gz)|
|Inception V3|Sparsity 54% (Magnitude)|ImageNet|77.87 (-0.03)|[inception_v3_imagenet_magnitude_sparsity.json](configs/sparsity/inception_v3_imagenet_magnitude_sparsity.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/inception_v3_sparsity_54.tar.gz)|
|Inception V3|INT8 w:sym,per-tensor a:sym,per-tensor + Sparsity 61% (RB)|ImageNet|77.58 (-0.32)|[inception_v3_imagenet_rb_sparsity_int8.json](configs/sparsity_quantization/inception_v3_imagenet_rb_sparsity_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/inception_v3_int8_w_sym_t_half_a_sym_t_rb_sparsity_61.tar.gz)|
|MobileNet V2|INT8 w:sym,per-tensor a:sym,per-tensor |ImageNet|71.66 (-0.19)|[mobilenet_v2_imagenet_int8.json](configs/quantization/mobilenet_v2_imagenet_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v2_int8_w_sym_t_half_a_sym_t.tar.gz)|
|MobileNet V2|Sparsity 50% (RB)|ImageNet|71.34 (-0.51)|[mobilenet_v2_imagenet_rb_sparsity.json](configs/sparsity/mobilenet_v2_imagenet_rb_sparsity.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v2_rb_sparsity_50.tar.gz)|
|MobileNet V2|int8 w:sym,per-tensor a:sym,per-tensor + sparsity 52% (RB)|ImageNet|71.0 (-0.85)|[mobilenet_v2_imagenet_rb_sparsity_int8.json](configs/sparsity_quantization/mobilenet_v2_imagenet_rb_sparsity_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v2_int8_w_sym_t_half_a_sym_t_rb_sparsity_52.tar.gz)|
|MobileNet V3 small|INT8 w:sym,per-channel a:asym,per-tensor |ImageNet|67.7 (-0.68)|[mobilenet_v3_small_imagenet_int8.json](configs/quantization/mobilenet_v3_small_imagenet_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v3_small_int8_w_sym_ch_half_a_asym_t.tar.gz)|
|MobileNet V3 small|INT8 w:sym,per-channel a:asym,per-tensor + Sparsity 42% (RB)|ImageNet|67.7 (-0.68)|[mobilenet_v3_small_imagenet_rb_sparsity_int8.json](configs/sparsity_quantization/mobilenet_v3_small_imagenet_rb_sparsity_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v3_small_int8_w_sym_ch_half_a_asym_t_rb_sparsity_42.tar.gz)|
|MobileNet V3 large|INT8 w:sym,per-channel a:asym,per-tensor |ImageNet|75.0 (-0.81)|[mobilenet_v3_large_imagenet_int8.json](configs/quantization/mobilenet_v3_large_imagenet_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v3_large_int8_w_sym_ch_half_a_asym_t.tar.gz)|
|MobileNet V3 large|INT8 w:sym,per-channel a:asym,per-tensor + Sparsity 42% (RB)|ImageNet|75.15 (-0.66)|[mobilenet_v3_large_imagenet_rb_sparsity_int8.json](configs/sparsity_quantization/mobilenet_v3_large_imagenet_rb_sparsity_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v3_large_int8_w_sym_ch_half_a_asym_t_sparsity_42.tar.gz)|
|ResNet50|INT8 w:sym,per-tensor a:sym,per-tensor|ImageNet|75.0 (-0.04)|[resnet50_imagenet_int8.json](configs/quantization/resnet50_imagenet_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/resnet50_int8_w_sym_t_half_a_sym_t.tar.gz)|
|ResNet50|Sparsity 80% (RB)|ImageNet|74.36 (-0.68)|[resnet50_imagenet_rb_sparsity.json](configs/sparsity/resnet50_imagenet_rb_sparsity.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/resnet50_rb_sparsity_80.tar.gz)|
|ResNet50|INT8 w:sym,per-tensor a:sym,per-tensor + Sparsity 65% (RB)|ImageNet|74.3 (-0.74)|[resnet50_imagenet_rb_sparsity_int8.json](configs/sparsity_quantization/resnet50_imagenet_rb_sparsity_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/resnet50_int8_w_sym_t_half_a_sym_t_rb_sparsity_65.tar.gz)|
|TensorFlow Hub MobileNet V2|Sparsity 35% (Magnitude)|ImageNet|71.90 (+0.06)|[mobilenet_v2_hub_imagenet_magnitude_sparsity.json](configs/sparsity/mobilenet_v2_hub_imagenet_magnitude_sparsity.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/tf1_mobilenet_v2_1.0_224_sparsity_35.tar.gz)|
|Inception V3|INT8 (per-tensor, symmetric for weights; per-tensor, symmetric for activations) |ImageNet|78.35 (-0.45)|[inception_v3_imagenet_int8.json](configs/quantization/inception_v3_imagenet_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/inception_v3_int8_w_sym_t_half_a_sym_t.tar.gz)|
|Inception V3|Sparsity 54% (Magnitude)|ImageNet|77.87 (0.03)|[inception_v3_imagenet_magnitude_sparsity.json](configs/sparsity/inception_v3_imagenet_magnitude_sparsity.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/inception_v3_sparsity_54.tar.gz)|
|Inception V3|INT8 (per-tensor, symmetric for weights; per-tensor, symmetric for activations) + Sparsity 61% (RB)|ImageNet|77.58 (0.32)|[inception_v3_imagenet_rb_sparsity_int8.json](configs/sparsity_quantization/inception_v3_imagenet_rb_sparsity_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/inception_v3_int8_w_sym_t_half_a_sym_t_rb_sparsity_61.tar.gz)|
|MobileNet V2|INT8 (per-tensor, symmetric for weights; per-tensor, symmetric for activations) |ImageNet|71.66 (0.19)|[mobilenet_v2_imagenet_int8.json](configs/quantization/mobilenet_v2_imagenet_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v2_int8_w_sym_t_half_a_sym_t.tar.gz)|
|MobileNet V2|Sparsity 50% (RB)|ImageNet|71.34 (0.51)|[mobilenet_v2_imagenet_rb_sparsity.json](configs/sparsity/mobilenet_v2_imagenet_rb_sparsity.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v2_rb_sparsity_50.tar.gz)|
|MobileNet V2|int8(per-tensor, symmetric for weights; per-tensor, symmetric for activations) + sparsity 52% (RB)|ImageNet|71.0 (0.85)|[mobilenet_v2_imagenet_rb_sparsity_int8.json](configs/sparsity_quantization/mobilenet_v2_imagenet_rb_sparsity_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v2_int8_w_sym_t_half_a_sym_t_rb_sparsity_52.tar.gz)|
|MobileNet V3 small|INT8 (per-channel, symmetric for weights; per-tensor, asymmetric for activations) |ImageNet|67.7 (0.68)|[mobilenet_v3_small_imagenet_int8.json](configs/quantization/mobilenet_v3_small_imagenet_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v3_small_int8_w_sym_ch_half_a_asym_t.tar.gz)|
|MobileNet V3 small|INT8 (per-channel, symmetric for weights; per-tensor, asymmetric for activations) + Sparsity 42% (RB)|ImageNet|67.7 (0.68)|[mobilenet_v3_small_imagenet_rb_sparsity_int8.json](configs/sparsity_quantization/mobilenet_v3_small_imagenet_rb_sparsity_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v3_small_int8_w_sym_ch_half_a_asym_t_rb_sparsity_42.tar.gz)|
|MobileNet V3 large|INT8 (per-channel, symmetric for weights; per-tensor, asymmetric for activations) |ImageNet|75.0 (0.81)|[mobilenet_v3_large_imagenet_int8.json](configs/quantization/mobilenet_v3_large_imagenet_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v3_large_int8_w_sym_ch_half_a_asym_t.tar.gz)|
|MobileNet V3 large|INT8 (per-channel, symmetric for weights; per-tensor, asymmetric for activations) + Sparsity 42% (RB)|ImageNet|75.15 (0.66)|[mobilenet_v3_large_imagenet_rb_sparsity_int8.json](configs/sparsity_quantization/mobilenet_v3_large_imagenet_rb_sparsity_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/mobilenet_v3_large_int8_w_sym_ch_half_a_asym_t_sparsity_42.tar.gz)|
|ResNet50|INT8 (per-tensor, symmetric for weights; per-tensor, symmetric for activations)|ImageNet|75.0 (0.04)|[resnet50_imagenet_int8.json](configs/quantization/resnet50_imagenet_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/resnet50_int8_w_sym_t_half_a_sym_t.tar.gz)|
|ResNet50|Sparsity 80% (RB)|ImageNet|74.36 (0.68)|[resnet50_imagenet_rb_sparsity.json](configs/sparsity/resnet50_imagenet_rb_sparsity.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/resnet50_rb_sparsity_80.tar.gz)|
|ResNet50|INT8 (per-tensor, symmetric for weights; per-tensor, symmetric for activations) + Sparsity 65% (RB)|ImageNet|74.3 (0.74)|[resnet50_imagenet_rb_sparsity_int8.json](configs/sparsity_quantization/resnet50_imagenet_rb_sparsity_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/resnet50_int8_w_sym_t_half_a_sym_t_rb_sparsity_65.tar.gz)|
|TensorFlow Hub MobileNet V2|Sparsity 35% (Magnitude)|ImageNet|71.90 (-0.06)|[mobilenet_v2_hub_imagenet_magnitude_sparsity.json](configs/sparsity/mobilenet_v2_hub_imagenet_magnitude_sparsity.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/tf1_mobilenet_v2_1.0_224_sparsity_35.tar.gz)|

#### Results for filter pruning
|**Model**|**Compression algorithm**|**Dataset**|**Accuracy (Drop) %**|**GFLOPS**|**MParams**|**NNCF config file**|**TensorFlow checkpoint**|
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|ResNet50|None|ImageNet|75.04|7.75 (100%)|25.5 (100%)|-|-|
|ResNet50|Filter Pruning 40%|ImageNet|74.98 (-0.06)|4.29 (55.35%)|15.8 (61.96%)|[Link](configs/pruning/resnet50_imagenet_pruning_geometric_median.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/resnet50_pruning_40.tar.gz)|
|ResNet50|Filter Pruning 40% + INT8 w:sym,per-tensor a:sym,per-tensor|ImageNet|75.08 (+0.04)|4.27 (55.10%)|15.8 (61.96%)|[Link](configs/pruning_quantization/resnet50_imagenet_pruning_geometric_median_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/resnet50_int8_w_sym_t_half_a_sym_t_pruning_40.tar.gz)|
|ResNet50|Filter Pruning 40%, geometric_median criterion|ImageNet|74.98 (0.06)|4.29 (55.35%)|15.8 (61.96%)|[Link](configs/pruning/resnet50_imagenet_pruning_geometric_median.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/resnet50_pruning_40.tar.gz)|
|ResNet50|Filter Pruning 40%, geometric_median criterion + INT8 (per-tensor, symmetric for weights; per-tensor, symmetric for activations)|ImageNet|75.08 (-0.04)|4.27 (55.10%)|15.8 (61.96%)|[Link](configs/pruning_quantization/resnet50_imagenet_pruning_geometric_median_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/resnet50_int8_w_sym_t_half_a_sym_t_pruning_40.tar.gz)|

#### Results for accuracy-aware compressed training

|**Model**|**Compression algorithm**|**Dataset**|**Accuracy (Drop) %**|**NNCF config file**|
| :---: | :---: | :---: | :---: | :---: |
|ResNet50|Sparsity 75% (magnitude)|ImageNet|74.42 (-0.62)|[resnet50_imagenet_magnitude_sparsity_accuracy_aware.json](configs/sparsity/resnet50_imagenet_magnitude_sparsity_accuracy_aware.json)|
|ResNet50|Sparsity 75% (magnitude)|ImageNet|74.42 (0.62)|[resnet50_imagenet_magnitude_sparsity_accuracy_aware.json](configs/sparsity/resnet50_imagenet_magnitude_sparsity_accuracy_aware.json)|
20 changes: 14 additions & 6 deletions examples/tensorflow/object_detection/README.md
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Expand Up @@ -12,6 +12,14 @@ The sample receives a configuration file where the training schedule, hyper-para
- Export to Frozen Graph or TensorFlow SavedModel that is supported by the OpenVINO™ toolkit.
- Distributed training on multiple GPUs on one machine is supported using [tf.distribute.MirroredStrategy](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy).

## Installation

To work with the sample you should install the corresponding Python package dependencies

```
pip install -r examples/tensorflow/requirements.txt
```

## Quantize Pretrained Model

This scenario demonstrates quantization with fine-tuning of RetinaNet with ResNet-50 backbone on the COCO2017 dataset.
Expand Down Expand Up @@ -147,15 +155,15 @@ To export a model to the OpenVINO IR and run it using the Intel® Deep Learning
|**Model**|**Compression algorithm**|**Dataset**|**mAP (drop) %**|**NNCF config file**|**TensorFlow checkpoint**|
| :---: | :---: | :---: | :---: | :---: | :---: |
|RetinaNet|None|COCO2017|33.44|[retinanet_coco.json](configs/retinanet_coco.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/retinanet.tar.gz)|
|RetinaNet|INT8 w:sym,per-tensor a:sym,per-tensor |COCO2017|33.22 (-0.22)|[retinanet_coco_int8.json](configs/quantization/retinanet_coco_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/retinanet_int8_w_sym_t_half_a_sym_t.tar.gz)|
|RetinaNet|Sparsity 50% (Magnitude)|COCO2017|33.13 (-0.31)|[retinanet_coco_magnitude_sparsity.json](configs/sparsity/retinanet_coco_magnitude_sparsity.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/retinanet_sparsity_50.tar.gz)|
|RetinaNet|INT8 (per-tensor, symmetric for weights; per-tensor, symmetric for activations)|COCO2017|33.22 (0.22)|[retinanet_coco_int8.json](configs/quantization/retinanet_coco_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/retinanet_int8_w_sym_t_half_a_sym_t.tar.gz)|
|RetinaNet|Sparsity 50% (Magnitude)|COCO2017|33.13 (0.31)|[retinanet_coco_magnitude_sparsity.json](configs/sparsity/retinanet_coco_magnitude_sparsity.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/retinanet_sparsity_50.tar.gz)|
|YOLOv4|None|COCO2017|47.04|[yolo_v4_coco.json](configs/yolo_v4_coco.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/yolo_v4.tar.gz)|
|YOLOv4|INT8 w:sym,per-channel a:asym,per-tensor |COCO2017|46.15 (-0.89)|[yolo_v4_coco_int8.json](configs/quantization/yolo_v4_coco_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/yolo_v4_int8_w_sym_ch_half_a_asym_t.tar.gz)|
|YOLOv4|Sparsity 50% (Magnitude)|COCO2017|46.54 (-0.50)|[yolo_v4_coco_magnitude_sparsity.json](configs/sparsity/yolo_v4_coco_magnitude_sparsity.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/yolo_v4_sparsity_50.tar.gz)|
|YOLOv4|INT8 (per-channel, symmetric for weights; per-tensor, asymmetric for activations)|COCO2017|46.15 (0.89)|[yolo_v4_coco_int8.json](configs/quantization/yolo_v4_coco_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/yolo_v4_int8_w_sym_ch_half_a_asym_t.tar.gz)|
|YOLOv4|Sparsity 50% (Magnitude)|COCO2017|46.54 (0.50)|[yolo_v4_coco_magnitude_sparsity.json](configs/sparsity/yolo_v4_coco_magnitude_sparsity.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/yolo_v4_sparsity_50.tar.gz)|
#### Results for filter pruning
|**Model**|**Compression algorithm**|**Dataset**|**mAP (drop) %**|**GFLOPS**|**MParams**|**NNCF config file**|**TensorFlow checkpoint**|
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|RetinaNet|None|COCO2017|33.44|194.1 (100%)|60.8 (100%)|[retinanet_coco.json](configs/retinanet_coco.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/retinanet.tar.gz)|
|RetinaNet|Filter Pruning 40%|COCO2017|32.7 (-0.74)|107.7 (55.49%)|34.7 (57.07%)|[retinanet_coco_pruning.json](configs/pruning/retinanet_coco_pruning.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/retinanet_pruning_40.tar.gz)|
|RetinaNet|Filter Pruning 40% + INT8 w:sym,per-tensor a:sym,per-tensor|COCO2017|32.82 (-0.62)|107.6 (55.44%)|34.7 (57.07%)|[retinanet_coco_pruning_int8.json](configs/pruning_quantization/retinanet_coco_pruning_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/retinanet_int8_w_sym_t_half_a_sym_t_pruning_40.tar.gz)|
|RetinaNet|Filter Pruning 40%, geometric_median criterion|COCO2017|32.7 (0.74)|107.7 (55.49%)|34.7 (57.07%)|[retinanet_coco_pruning.json](configs/pruning/retinanet_coco_pruning.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/retinanet_pruning_40.tar.gz)|
|RetinaNet|Filter Pruning 40%, geometric_median criterion + INT8 (per-tensor, symmetric for weights; per-tensor, symmetric for activations)|COCO2017|32.82 (0.62)|107.6 (55.44%)|34.7 (57.07%)|[retinanet_coco_pruning_int8.json](configs/pruning_quantization/retinanet_coco_pruning_int8.json)|[Link](https://storage.openvinotoolkit.org/repositories/nncf/tensorflow/models/develop/retinanet_int8_w_sym_t_half_a_sym_t_pruning_40.tar.gz)|
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