Comments (1)
Yeah collate is very useful for zero-padding your samples when there's a different number of objects (or lengths in other cases).
But in my experience, it's specific to each problem. I'm not sure how it could generalize across all datasets if we add that to the DataLoader.
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Related Issues (20)
- Allow Unseeded Training HOT 3
- Change x-axis of validation accuracy HOT 1
- Seed Numpy in addition to Torch HOT 1
- Clip stride, clip offset, and num clips are unimplemented HOT 1
- Add preprocessing for x-y translation
- Variable clip length HOT 2
- Remove feature extraction code from c3d HOT 1
- Reset validation accuracy every epoch
- Add author accuracies in Readme
- Create outline for usage of ViP
- RuntimeError: Given input size: (512x1x7x7). Calculated output size: (512x0x4x4). Output size is too small HOT 1
- config error HOT 3
- Support for multi-gpu training & evaluation
- Pytorch definition of optimizers and schedulers
- Separate each metric and loss into files
- About gen_json_UCF101 HOT 1
- Where should I put the dataset?
- Can you provide the link for 2.2 times cropped dataset of MPII+NZSL hand dataset? HOT 3
- No such file or directory: '/z/dat/HMDB51/train.json' HOT 1
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