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convert_darknet_yolo_to_tensorflow's Issues

Speed decreases significantly for real time inference

Hi there,
Thank you for this amazing repo. I tried converting my custom object detection model using this repo and while testing I found out the inference speed decreased from 25-35 FPS to 1-2.5 FPS which is really poor. I need to convert the darknet model to tf js and for that, I tried converting it to the Tensorflow model first. Do have any idea how I can keep the speed and accuracy and convert the weights to Tensorflow/TF JS model?

Duplicated quantization flag assignment makes impossible to create a int8 quantized model

converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]

The second assignment to converter.target_spec.supported_ops supersedes the first one that contains the int8 flag

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