config = dict() # There are 342176 images in train set # There are 38019 images in validation set config["total_train"] = 342176 config["total_test"] = 38019 config["batch_size"] = 64 # calculate the validate every based on the number of available data config["validate_every"] = int( config["total_train"] / config["batch_size"] ) # Usually equal to one epoch config["validate_for"] = int(config["total_test"] / config["batch_size"]) config["save_every"] = 3 * config["validate_every"] # number of epochs config["total_steps"] = config["validate_every"] * 60 # BASIC MODEL hyperparameters config["n_filters"] = [16, 32, 128, 128, 256, 256] config["filter_sizes"] = [3, 3, 3, 3, 3, 3] config["max_pool"] = [1, 1, 1, 1, 1, 1] config["fc_layers"] = [256, 128] # # OPTIMIZATION hyperparameters config["learning_rate"] = [ 0.001, 0.0009, 0.0006, 0.0003, 0.0001, 0.00005, 0.00001, 0.000005, 0.000001, ] config["decay_rate"] = 0.96 # Usually decay every half of epochs config["decay_step"] = 5 * config["validate_every"] config["optimizer"] = "ADAM" config["keep_prob"] = 0.85 config["MAX_GRADIANT_NORM"] = 5.0 # L2 regularization config["l2_beta"] = 0.0005 # input info config["input_width"] = 192 config["input_height"] = 192 config["input_channel"] = 1 # Output shape config["output_dim"] = 3 config["output_weights"] = [1.0, 1.0, 1.0, 1.0, 0.5] # Augmentation parameters config["prob_downscale"] = 0.75 config["max_downscale"] = 0.95 config["min_downscale"] = 0.5 config["prob_reflection"] = 0.25 config["min_reflection"] = 0.25 config["max_reflection"] = 0.75 config["prob_blur"] = 0.25 config["min_blurSize"] = 3 config["max_blurSize"] = 9 config["min_sigmaRatio"] = 0.25 config["max_sigmaRatio"] = 0.75 # config["prob_occlusion"] = 0.5 config["min_occlusion"] = 0.05 config["max_occlusion"] = 0.25 config["occlusion_max_obj"] = 6 # exposure on noisy frames config["prob_exposure"] = 0.25 config["min_exposure"] = 0.7 config["max_exposure"] = 1.2 # crop input image config["crop_probability"] = 0.5 config["crop_min_ratio"] = 0.5 config["crop_max_ratio"] = 0.95 # flip image config["flip_probability"] = 0.5 # add Pupil config["prob_pupil"] = 0.25