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Benchamrk on paper PixelFormer: Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention

  1. Download InSpaceType eval set. Install torch and torchivsion and packages: matplotlib, tqdm, tensorboardX, timm, mmcv, opencv-python

  2. Download pretrained model 'nyu.pt' from Official Link and put it here

python pixelformer/test.py --data_path ./ --dataset nyu --filenames_file data_splits/split_files.txt --checkpoint_path nyu.pth --max_depth 10

The command generates report files for hierarchy (H0-H2). *-all means overall H0-H2 means level of hierarchy. H1_xx means scene space type number. See space_type_def.yml for space type number definition.