tf-lite

问题 I've been studying quantization using Tensorflow's TFLite. As far as I understand it is possible to quantize my model weights (so that they will be stored using 4x less memory) but it doesn't necessary implies that the model won't convert it back to floats to run it. I've also understood that to run my model only using int I need to set the following parameters: converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.inference_input_type = tf.uint8 converter

问题 I am trying out tflite C++ API for running a model that I built. I converted the model to tflite format by following snippet: import tensorflow as tf converter = tf.lite.TFLiteConverter.from_keras_model_file('model.h5') tfmodel = converter.convert() open("model.tflite", "wb").write(tfmodel) I am following the steps provided at tflite official guide, and my code upto this point looks like this // Load the model std::unique_ptr<tflite::FlatBufferModel> model = tflite::FlatBufferModel:

问题 I am trying to run a tflite model on Android for object detection. For the same, I have successfully trained the model with my sets of images as follows: (a) Training: !python3 object_detection/model_main.py \ --pipeline_config_path=/content/drive/My\ Drive/Detecto\ Tutorial/models/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config \ --model_dir=training/ (modifying the config file to point to where my specific TFrecords are mentioned) (b) Export inference graph !python

问题 I am trying to run a tflite model on Android for object detection. For the same, I have successfully trained the model with my sets of images as follows: (a) Training: !python3 object_detection/model_main.py \ --pipeline_config_path=/content/drive/My\ Drive/Detecto\ Tutorial/models/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config \ --model_dir=training/ (modifying the config file to point to where my specific TFrecords are mentioned) (b) Export inference graph !python

问题 I had lost my dataset by a careless mistake. I have only my tflite file left in my hand. Is there any solution to reverse back h5 file. I have been done decent research in this but no solutions found. 回答1: The conversion from a TensorFlow SaveModel or tf.keras H5 model to .tflite is an irreversible process. Specifically, the original model topology is optimized during the compilation by the TFLite converter, which leads to some loss of information. Also, the original tf.keras model's loss and

来源： https://stackoverflow.com/questions/64030807/standard-tflite-object-detection-model-not-working-in-mlkit

来源： https://stackoverflow.com/questions/64030807/standard-tflite-object-detection-model-not-working-in-mlkit

来源： https://stackoverflow.com/questions/64030807/standard-tflite-object-detection-model-not-working-in-mlkit

来源： https://stackoverflow.com/questions/63830570/full-quatization-does-not-except-int8-data-to-change-model-input-layer-to-int8

来源： https://stackoverflow.com/questions/63108767/create-training-graph-failed-when-converted-mobilefacenet-to-quantize-aware-mo