About

We provide a set of transfer learning-based model training and evaluation functions. ImageNet pretrained weights are used.
The series of Efficient Net models are supported. There is always a trade-off between model size and accuracy. Our guideline is as follows:
For tfjs apps, use EfficientNetB0 or EfficientNetB1; For tf-lite apps, use EfficientNetB2 ~ B4; For desktop apps, use EfficientNetB5 and above.

The following table is from keras website:

Install

pip install tensorflow-gpu >= 2
pip install tlearner

How to use

1. Initialize

   from tlearner.efficientnet import transfer_learner learner = transfer_learner("my_model", W = 224) # my_model.h5df will be the model name

2. Load data

   learner.load_dataset('my_data_folder', PKL_PATH = "my_dataset.pkl") # Images in each subfolder of my_data_folder are treated as one class. If PKL_PATH is specified, a pkl that contains X_train, y_train, X_val, y_val, etc. will be generated.
   print(learner.class_names) # print class names

3. Train a new model

   hist = learner.train_custom_model("EfficientNetB1", batch = 8, epochs = [10,0], optimizer = "adam") # EfficientNetB0-B7 can be used.
   plot_history(hist) # plot training curves

4. Evaluate

   learner.evaluate(N = 30) # predict the first N samples in X_val

5. Predict

   learner.predict_file('my_image.jpg')

6. Convert [Optional]

   learner.convert_to_tflite(v1 = True) # convert to a local tflite model

7. Load an existing model

   learner.load_best_model() # if you have an existing local model
   learner.get_best_model().summary() # print model architecture
   learner.plot_best_model() # save model architecture to a local image file

Jupyter notebooks

Under /notebooks, we provide two examples. One is flower image classification; the other is fundus image classification.

Deployment

After training, you will get a keras h5 model file. You can further convert it to tflite format, or tfjs format (efficient net is not supported yet).
Then you can deploy on mobile device or browser-based apps.