Comments (1)
huang-ziyuan
commented on June 24, 2024
This is probably caused by the same problem as we previously discussed about the test-time cropping.
We originally used a different function for resizing and cropping from the videos (see here). Now we have changed to KineticsResizedCrop. The logics for resizing and cropping are different, which might be the underlying reason for the performance mismatch. You could try modifying the code to match previous strategies, or change the configs for the current strategy to match the behaviour of our previous strategy.
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Related Issues (20)
- Issue on running on sthv2 HOT 2
- Problems on loading imagenet weight HOT 1
- features HOT 2
- Use TAdaConv in Video Object Detection? HOT 1
- How to use R(2+1)D with TAda? HOT 1
- some questions about flops and inference time HOT 1
- TAdaConvNeXt-T HOT 3
- Frame description and temporal modeling HOT 1
- Applying TadaConv HOT 1
- Batch-wise and Temporal-wise modeling HOT 1
- I am curious about why the flops and parms of TAdaConvNeXt-T is one half of the ResNet50's, to my knowledge they should be similar. HOT 2
- apply AdaConv to P3D or SlowFast HOT 1
- Applying TAdaConv3d to Timesformer HOT 2
- top1-5 accuracy did not achieve the expected effect（Mosi/Finetuned on UCF101/HMDB51 dataset） HOT 12
- TAdaConv2d needs in_channels to equal out_channels HOT 1
- Enquiry on the batch size when using 32-frames HOT 2
- Feedback on the issue of ema coefficient. HOT 5
- Question on reproducing the results on sthv2
- RuntimeError: Error(s) in loading state_dict for BaseVideoModel: size mismatch for head.out.weight: copying a param with shape torch.Size([400, 768]) from checkpoint, the shape in current model is torch.Size([5, 768]). size mismatch for head.out.bias: copying a param with shape torch.Size([400]) from checkpoint, the shape in current model is torch.Size([5]). HOT 1
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