nest import NesT nest = NesT( image_size = 224, patch_size = 4, dim = 96, heads = 3, num_hierarchies = 3, # number of hierarchies block_repeats = (2, 2, 8), # the number of transformer blocks at each hierarchy, starting from the bottom num_classes = 1000 ) img = torch....
Most importantly, we downsample the original 13 classes to 8 by combining several classes into one. Pre-process CholecSeg8k by running: python ./datasets/CholecSeg8k/utils/preprocess_CholecSeg8k_multi_process.py \ --data_dir ./datasets/CholecSeg8k/data \ --output_dir ./datasets/CholecSeg...
Before you use the code to train your own data set, please first enter thetrain_gpu.pyfile and modify thedata_root,batch_size,data_len,num_workersandnb_classesparameters. If you want to draw the confusion matrix and ROC curve, you only need to set thepredictparameter toTrue. ...
nest import NesT nest = NesT( image_size = 224, patch_size = 4, dim = 96, heads = 3, num_hierarchies = 3, # number of hierarchies block_repeats = (2, 2, 8), # the number of transformer blocks at each hierarchy, starting from the bottom num_classes = 1000 ) img = torch....
nest import NesT nest = NesT( image_size = 224, patch_size = 4, dim = 96, heads = 3, num_hierarchies = 3, # number of hierarchies block_repeats = (2, 2, 8), # the number of transformer blocks at each hierarchy, starting from the bottom num_classes = 1000 ) img = torch....
nest import NesT nest = NesT( image_size = 224, patch_size = 4, dim = 96, heads = 3, num_hierarchies = 3, # number of hierarchies block_repeats = (2, 2, 8), # the number of transformer blocks at each hierarchy, starting from the bottom num_classes = 1000 ) img = torch....
nest import NesT nest = NesT( image_size = 224, patch_size = 4, dim = 96, heads = 3, num_hierarchies = 3, # number of hierarchies block_repeats = (2, 2, 8), # the number of transformer blocks at each hierarchy, starting from the bottom num_classes = 1000 ) img = torch....
nest import NesT nest = NesT( image_size = 224, patch_size = 4, dim = 96, heads = 3, num_hierarchies = 3, # number of hierarchies block_repeats = (2, 2, 8), # the number of transformer blocks at each hierarchy, starting from the bottom num_classes = 1000 ) img = torch....