Remove uneeded *py tutorials and edit the tutorials readm #1002
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ofirgo
requested changes
Mar 19, 2024
tutorials/README.md
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| ## Getting started | ||
| This "hello world" notebook shows how to quickly quantize a pre-trained model using MCT post training quantization technique both for Keras models and Pytorch models. | ||
| - [Keras MobileNetV2 post training quantization](notebooks/keras/ptq/example_keras_imagenet.ipynb) | ||
| - [Pytorch MobileNetV2 post training quantization](notebooks/pytorch/ptq/example_pytorch_mobilenet_v2.py) |
tutorials/README.md
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| - [Pytorch MobileNetV2 post training quantization](notebooks/pytorch/ptq/example_pytorch_mobilenet_v2.py) | ||
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| ## MCT Features | ||
| In this section, we will cover more advanced topics related to quantization. |
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"This set of tutorials covers all the quantization tools provided by MCT. The notebooks in this section demonstrate how to configure and run simple and advanced post-training quantization methods. This includes..."
tutorials/README.md
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| In this section, we will cover more advanced topics related to quantization. | ||
| This includes fine-tuning PTQ (Post-Training Quantization) configurations, exporting models, | ||
| and exploring advanced compression techniques. | ||
| These techniques are crucial for optimizing models further and achieving better performance in deployment scenarios. |
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change crucial to benefitial and rephrase (try to consult chatgpt maybe for making it a little more shiny)
| ## Quantization for Sony-IMX500 deployment | ||
| This section provides a guide on quantizing pre-trained models to meet specific constraints for deployment on the | ||
| processing platform. Our focus will be on quantizing models for deployment on [Sony-IMX500](https://developer.sony.com/imx500/) processing platform. | ||
| We will cover various tasks and demonstrate the necessary steps to achieve efficient quantization for optimal |
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This is not a tutorial but an introduction, the future tense here is mistaken.
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| Here we provide examples on quantizing pre-trained models for deployment on Sony-IMX500 processing platform. | ||
| We will cover various tasks and demonstrate the necessary steps to achieve efficient quantization for optimal | ||
| deployment performance. |
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highlight that the exported models from these tutorials are ready to be deployed on IMX500! (plug-and-play)
ofirgo
approved these changes
Mar 21, 2024
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Pull Request Description:
Remove unneeded tutorials and edit the tutorials readme
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