From 97ea0ca42645db6d2e724b50f78e553eb2927b96 Mon Sep 17 00:00:00 2001 From: Grzegorz Latosinski Date: Wed, 13 Mar 2024 12:09:44 +0100 Subject: [PATCH] l02_quantization: README.md: Minor update Signed-off-by: Grzegorz Latosinski --- dl_in_iot_course/l02_quantization/README.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/dl_in_iot_course/l02_quantization/README.md b/dl_in_iot_course/l02_quantization/README.md index 51f3faa..f6350f4 100644 --- a/dl_in_iot_course/l02_quantization/README.md +++ b/dl_in_iot_course/l02_quantization/README.md @@ -30,6 +30,7 @@ It requires implementing methods for: * in `optimize_model`, load the trained model, create a `tf.lite.TFLiteConverter` object from the model, convert it to the TFLite model without any optimizations and save results to the file under `self.modelpath` path. * in `prepare_model`, create a `tf.lite.Interpreter` for the model saved in `self.modelpath` path. I'd suggest setting a `num_threads` parameter here to the number of threads available in the CPU to significantly boost the inference process. + You can use e.g. `multiprocessing` module to collect number of available cores. Remember to allocate tensors (there is a method for it). * in `run_inference` method, invoke the model. * in `postprocess_outputs`, implement the method for getting the output tensor (check out `preprocess_input` method for hints on how to do it). @@ -99,11 +100,9 @@ Additional factors: * `[2pt]` Git history quality -`NOTE:` the INT8 models may actually perform slower than FP32 models on x86_64 CPUs. +`NOTE:` There is no need to include the models in the repository. -`NOTE:` there is no need to include the models in the repository. - -`NOTE:` confusion matrix shows clearly if there are any issues with the optimized model. +`NOTE:` Confusion matrix shows clearly if there are any issues with the optimized model. If the confusion matrix is almost random (with no significantly higher values along the diagonal) - there are possible issues with the model, usually within preprocessing step (make sure to use `scale`, `zero_point` parameters and to convert the input data to `int8` type). ## Resources