Training and export TFLite models for image classification with streamlit

models such as EfficientNet-Lite* models, MobileNetV2, ResNet50 as pre-trained models for image classification.

EfficientNet-Lite are a family of image classification models that could achieve state-of-art accuracy and suitable for Edge devices. The default model is EfficientNet-Lite0.

Change the training hyperparameters We could also change the training hyperparameters like epochs, dropout_rate and batch_size that could affect the model accuracy. The model parameters you can adjust are:

epochs: more epochs could achieve better accuracy until it converges but training for too many epochs may lead to overfitting. dropout_rate: The rate for dropout, avoid overfitting. None by default. batch_size: number of samples to use in one training step. None by default. validation_data: Validation data. If None, skips validation process. None by default. train_whole_model: If true, the Hub module is trained together with the classification layer on top. Otherwise, only train the top classification layer. None by default. learning_rate: Base learning rate. None by default. momentum: a Python float forwarded to the optimizer. Only used when use_hub_library is True. None by default. shuffle: Boolean, whether the data should be shuffled. False by default. use_augmentation: Boolean, use data augmentation for preprocessing. False by default. use_hub_library: Boolean, use make_image_classifier_lib from tensorflow hub to retrain the model. This training pipeline could achieve better performance for complicated dataset with many categories. True by default. warmup_steps: Number of warmup steps for warmup schedule on learning rate. If None, the default warmup_steps is used which is the total training steps in two epochs. Only used when use_hub_library is False. None by default. model_dir: Optional, the location of the model checkpoint files. Only used when use_hub_library is False. None by default. Parameters which are None by default like epochs will get the concrete default parameters in make_image_classifier_lib from TensorFlow Hub library or train_image_classifier_lib.

https://ai.google.dev/edge/litert/libraries/modify/image_classification