sayakpaul / videomae-base-finetuned-ucf101-subset

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Total runs: 3
24-hour runs: 0
7-day runs: 0
30-day runs: -3
Model's Last Updated: November 12 2022
video-classification

Introduction of videomae-base-finetuned-ucf101-subset

Model Details of videomae-base-finetuned-ucf101-subset

videomae-base-finetuned-ucf101-subset

This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3992
  • Accuracy: 0.8645
Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure
Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 148
Training results
Training Loss Epoch Step Validation Loss Accuracy
2.1374 0.26 38 1.7413 0.5714
0.7949 1.26 76 0.7747 0.8
0.4279 2.26 114 0.4053 0.9143
0.291 3.23 148 0.3429 0.9286
Framework versions
  • Transformers 4.24.0
  • Pytorch 1.12.1+cu113
  • Datasets 2.6.1
  • Tokenizers 0.13.2

Runs of sayakpaul videomae-base-finetuned-ucf101-subset on huggingface.co

3
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0
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0
3-day runs
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videomae-base-finetuned-ucf101-subset install

videomae-base-finetuned-ucf101-subset is an open source model from GitHub that offers a free installation service, and any user can find videomae-base-finetuned-ucf101-subset on GitHub to install. At the same time, huggingface.co provides the effect of videomae-base-finetuned-ucf101-subset install, users can directly use videomae-base-finetuned-ucf101-subset installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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