![]() ![]() # Make sure that `MODEL_TYPE` is correctly set. Metric = accuracy if not multilabel else hamming_accuracy Multilabel = is_data_multilabel(DATA_PATH) # This function assumes that your multi-label dataset is structured in the recommended format shown in the (02_multilabel_classification.ipynb). You can inspect the function by calling `is_data_multilabel?`. In order to detect whether or not a dataset is multi-label, the helper function will check to see if the datapath contains a csv file that has a column 'labels' where the values are space-delimited. To do so, we'll use the `is_data_multilabel` helper function. IM_SIZE = 300 # We'll automatically determine if your dataset is a multi-label or traditional (single-label) classification problem. ![]() IM_SIZE = 300 if MODEL_TYPE = "small_size": IM_SIZE = 500 if MODEL_TYPE = "fast_inference": # Set parameters based on your selected model. = True return model_meta.get(arch, _default_meta) _default_meta = ,ĭef cnn_config( arch): "Get the metadata associated with `arch`." # Split squeezenet model on maxpool layers def _squeezenet_split( m:nn.Module): return (m, m, m)ĭef _densenet_split( m:nn.Module): return (m,m)ĭef _vgg_split( m:nn.Module): return (m,m)ĭef _alexnet_split( m:nn.Module): return (m,m) ![]()
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