Comments (1)
Hi, having at least 12GB of GPU memory is the requirement for this implementation. The memory bottleneck are the context frames: depending on the test set, there may be up to 20 previous predictions accumulated in the context. Those have to stay on GPU memory. Obviously, you can move some of the computations on the CPU, but that would dramatically slow down the inference (and it's already quite slow on GPU). Nikita
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Related Issues (14)
- colab HOT 1
- issue when training the ResNet-18 model
- Are you training your own model or a pretrained model (Resnet18) ? HOT 1
- all data not found when training
- Couldn't download the filelists
- Add indexing='ij' in torch.meshgrid()
- train on youtube-vos HOT 3
- incomplete training samples when using YoutubeVOS dataset
- Train on custom video data HOT 2
- Request of the performance deviation when training with YouTube-VOS HOT 3
- (Maybe) Redundant code implementation in train.py HOT 1
- TypeError: forward() missing 1 required positional argument: 'frames' HOT 1
- inference error HOT 1
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