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Pytorch Feature Tutorials Refactor (sony#1225)
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Refactored feature notebooks for consistency. Standardized function usage across notebooks. Updated model examples to use pretrained models from torchvision with the ImageNet validation set as the representative dataset (except for the pruning notebook).

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Co-authored-by: liord <[email protected]>
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lior-dikstein and liord authored Sep 26, 2024
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28 changes: 16 additions & 12 deletions tutorials/notebooks/mct_features_notebooks/README.md
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Expand Up @@ -72,15 +72,11 @@ These techniques are essential for further optimizing models and achieving super
<details id="pytorch-ptq">
<summary>Post-Training Quantization (PTQ)</summary>

| Tutorial | Included Features |
|---------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------|
| [Training & Quantizing Model on MNIST](pytorch/example_pytorch_ptq_mnist.ipynb) | &#x2705; PTQ |
| [Mixed-Precision MobileNetV2 on Cifar100](pytorch/example_pytorch_mobilenetv2_cifar100_mixed_precision.ipynb) | &#x2705; PTQ <br/> &#x2705; Mixed-Precision |
| [SSDLite MobileNetV3 Quantization](pytorch/example_pytorch_ssdlite_mobilenetv3_object_detection.ipynb) | &#x2705; PTQ |

</details>


| Tutorial | Included Features |
|-----------------------------------------------------------------------------------------------------------|---------------------------------------------|
| [Basic Post-Training Quantization (PTQ)](pytorch/example_pytorch_post_training_quantization.ipynb) | &#x2705; PTQ |
| [Mixed-Precision Post-Training Quantization](pytorch/example_pytorch_mixed_precision_ptq.ipynb) | &#x2705; PTQ <br/> &#x2705; Mixed-Precision |
| [Advanced Gradient-Based Post-Training Quantization (GPTQ)](pytorch/example_pytorch_mobilenet_gptq.ipynb) | &#x2705; GPTQ |

</details>

Expand All @@ -97,9 +93,9 @@ These techniques are essential for further optimizing models and achieving super
<details id="pytorch-data-generation">
<summary>Data Generation</summary>

| Tutorial | Included Features |
|-----------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|
| [Data-Free Quantization using Data Generation](pytorch/example_pytorch_data_generation.ipynb) | &#x2705; PTQ <br/> &#x2705; Data-Free Quantization <br/> &#x2705; Data Generation |
| Tutorial | Included Features |
|-----------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
| [Zero-Shot Quantization (ZSQ) using Data Generation](pytorch/example_pytorch_data_generation.ipynb) | &#x2705; PTQ <br/> &#x2705; ZSQ <br/> &#x2705; Data-Free Quantization <br/> &#x2705; Data Generation |

</details>

Expand All @@ -112,3 +108,11 @@ These techniques are essential for further optimizing models and achieving super
| [Exporter Usage](pytorch/example_pytorch_export.ipynb) | &#x2705; Export |

</details>
<details id="pytorch-xquant">
<summary>Quantization Troubleshooting</summary>

| Tutorial | Included Features |
|------------------------------------------------------------------------------------------------|-------------------|
| [Quantization Troubleshooting using the Xquant Feature](pytorch/example_pytorch_xquant.ipynb) | &#x2705; Debug |

</details>
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