pytorch-retraining Transfer Learning shootout for PyTorch's model zoo (torchvision). Load any pretrained model with custom final layer (num_classes) from PyTorch's model zoo in one line model_pretrained, diff = load_model_merged('inception_v3', num_classes) Retrain minimal (as inferred on load) or a custom amount of layers on multiple GPUs. Optionally with Cyclical Learning Rate (Smith 2017). final_param_names = [d[0] for d in diff] stats = train_eval(model_pretrained, trainloader, testloader, final_params_names) Chart training_time, evaluation_time (fps), top-1 accuracy for varying levels of retraining depth (shallow, deep and from scratch) Transfer learning on example dataset Bee vs Ants with 2xV100 GPUs Results on more elaborate Dataset num_classes = 23, slightly unbalanced, high variance in rotation and motion blur artifacts with 1xGTX1080Ti Constant LR with momentum Cyclical Learning Rate
