13 lines
823 B
Markdown
13 lines
823 B
Markdown
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# Model Quantifization
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Int8 quantization is one of the key features in [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).
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It supports two kinds of training aware, **Dynamic strategy** and **Static strategy**,
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layer-wise and channel-wise quantization,
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and using PaddleLite to deploy models generated by PaddleSlim.
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By using this toolkit, [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) quantized the mobilenet_v3_large_x1_0 model whose accuracy is 78.9% after distilled.
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After quantized, the prediction speed is accelerated from 19.308ms to 14.395ms on SD855.
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The storage size is reduced from 21M to 10M.
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The top1 recognition accuracy rate is 75.9%.
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For specific training methods, please refer to [PaddleSlim quant aware](https://paddlepaddle.github.io/PaddleSlim/quick_start/quant_aware_tutorial.html)。
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