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videomae-base-finetuned-bimanual-subset

sano90 / videomae-base-finetuned-bimanual-subset

Run on huggingface.co
huggingface.co
Total runs: 295
24-hour runs: 1
7-day runs: 5
30-day runs: 284
Model's Last Updated: June 04 2025
video-classification

Introduction of videomae-base-finetuned-bimanual-subset

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

videomae-base-finetuned-bimanual-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: 0.0695
  • Accuracy: 0.9813
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 550
Training results
Training Loss Epoch Step Validation Loss Accuracy
2.1402 0.0218 12 2.0213 0.3186
2.0459 1.0218 24 1.9679 0.1601
1.8931 2.0218 36 1.9863 0.2436
1.9829 3.0218 48 2.1146 0.2112
1.6759 4.0218 60 1.9753 0.2572
1.7893 5.0218 72 2.3825 0.0920
1.5049 6.0218 84 1.6753 0.3509
1.3212 7.0218 96 1.2506 0.4191
1.177 8.0218 108 0.9863 0.6934
0.9292 9.0218 120 0.8076 0.7479
0.7332 10.0218 132 0.8698 0.7036
0.7974 11.0218 144 1.0144 0.6371
0.5493 12.0218 156 0.7424 0.7223
0.5826 13.0218 168 0.6612 0.7513
0.4888 14.0218 180 0.6969 0.7462
0.5257 15.0218 192 1.1179 0.5945
0.4575 16.0218 204 0.4811 0.8552
0.3962 17.0218 216 0.7893 0.7411
0.3146 18.0218 228 0.5508 0.8279
0.2503 19.0218 240 0.8989 0.7411
0.3556 20.0218 252 0.2343 0.9182
0.1748 21.0218 264 0.3174 0.9148
0.0848 22.0218 276 0.4301 0.8756
0.3159 23.0218 288 0.6723 0.7888
0.1157 24.0218 300 0.3365 0.8825
0.0756 25.0218 312 0.2978 0.9131
0.0333 26.0218 324 0.3446 0.8995
0.0799 27.0218 336 0.1619 0.9472
0.0223 28.0218 348 0.6790 0.8075
0.0888 29.0218 360 0.1496 0.9591
0.1218 30.0218 372 0.2766 0.9165
0.2018 31.0218 384 0.2846 0.9114
0.2506 32.0218 396 0.1404 0.9540
0.1235 33.0218 408 0.3010 0.9012
0.134 34.0218 420 0.0710 0.9796
0.0149 35.0218 432 0.1010 0.9693
0.0142 36.0218 444 0.1540 0.9540
0.0383 37.0218 456 0.1624 0.9557
0.0627 38.0218 468 0.1210 0.9693
0.1269 39.0218 480 0.0801 0.9796
0.1244 40.0218 492 0.0695 0.9813
0.083 41.0218 504 0.1012 0.9779
0.0674 42.0218 516 0.1151 0.9727
0.0715 43.0218 528 0.1084 0.9727
0.0972 44.0218 540 0.1047 0.9727
0.0068 45.0182 550 0.1054 0.9710
Framework versions
  • Transformers 4.46.3
  • Pytorch 2.0.1+cu117
  • Datasets 3.1.0
  • Tokenizers 0.20.3

Runs of sano90 videomae-base-finetuned-bimanual-subset on huggingface.co

295
Total runs
1
24-hour runs
0
3-day runs
5
7-day runs
284
30-day runs

More Information About videomae-base-finetuned-bimanual-subset huggingface.co Model

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videomae-base-finetuned-bimanual-subset huggingface.co

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videomae-base-finetuned-bimanual-subset install

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

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Provider of videomae-base-finetuned-bimanual-subset huggingface.co

sano90
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