Comments (1)
Hi @jzhang38! Thanks for your question! It takes about 27 hours to pre-train our VideoMAE (ViT-B) for 800 epochs on Kinetics-400 using 64 Tesla V100 GPUs. If you find the GPU-Util
is not high enough, please reduce the batch-size to alleviate the pressure of your CPUs and I/O. Set --batch_size
to 16 can also give favorable results.
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Related Issues (20)
- What's the finetuning differences between ViT-B 80%acc and 81%acc?
- Can I fine-tine it on a video dataset of 32 frames?
- The Batch size and training epoch not metch with paper
- About the encoder layer output
- Issue Encountered When Loading the Model: "pretrain_videomae_base_patch16_224" HOT 5
- learnable position embedding HOT 1
- the acc of small batch datasets is too low HOT 4
- Can VideoMAE be used to learn the motion characteristics and appearance characteristics of objects in videos?
- MoCoV3 Training Configuration
- ViT-S and ViT-H models on huggingface
- VideoMAE ViT-H pre-train does not contain the decoder weights HOT 2
- dist_init_required HOT 1
- About pre-trained models
- Fail to finetune from the provided pretrained model checkpoint on UCF101 HOT 5
- How many videos are in your validation set? HOT 3
- could you please provide me the weight of VideoMAE pre-trained on Kinetics-400,I want to use the the weight to extract the features of the thumos14 HOT 4
- The dataset files in the link are not available HOT 2
- BUG: Incorrect temporal indexing?
- UCF101 HOT 4
- Questions about performence on ssv2
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