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> [TFLite backend](https://www.tensorflow.org/lite/performance/quantization_spec) : Per-axis (aka per-channel in Conv ops) or per-tensor weights are represented by int8 two’s complement values in the range [-127, 127] with zero-point equal to 0....
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It is advisable to first attempt Post-Training Quantization ([PTQ](https://github.com/alibaba/TinyNeuralNetwork/blob/main/examples/quantization/post.py)) to quickly observe the loss introduced by quantization. If the results of PTQ are devastating, such as a drop of more...
Alternatively, could you share the YOLOv8 model file (or the open-source repository you used) and your QAT training script?
If the rewritten model's mAPs has no difference with the original model, I strongly recommend you first use the rewritten model(the py file and pth file in out dir) to...
``` with model_tracer(): model = YOLO("yolov8n-cls.pt").model dummy_input = torch.rand((1, 3, 224, 224)) quantizer = PostQuantizer(model, dummy_input, work_dir='out') ptq_model = quantizer.quantize() ``` this use case can be traced correctly.
Hi @hoangtv2000 , you can try to quantize Yolov8 detection model as below: 1. You need to use PostQuantizer to properly trace the complete model in eval mode. - Modify...
So, how about the fake-quantized qat-prepared model's mAPs before training which I mentioned at https://github.com/alibaba/TinyNeuralNetwork/issues/337#issuecomment-2205330949 step3