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videomae-base-finetuned-isl-numbers

Video Classification
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metadata
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: videomae-base-finetuned-isl-numbers
    results: []

videomae-base-finetuned-isl-numbers

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: 1.1287
  • Accuracy: 0.6444

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: 1100

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.4795 0.02 22 2.4767 0.0256
2.4249 1.02 44 2.4351 0.1026
2.4561 2.02 66 2.4196 0.1026
2.3841 3.02 88 2.3735 0.1026
2.5186 4.02 110 2.4258 0.0769
2.3806 5.02 132 2.3214 0.1538
2.3579 6.02 154 2.2858 0.1538
2.2955 7.02 176 2.1729 0.1795
2.1351 8.02 198 1.9503 0.3333
2.1626 9.02 220 2.1922 0.2051
2.0905 10.02 242 1.8453 0.3333
1.7091 11.02 264 1.6305 0.4872
1.6316 12.02 286 1.6529 0.3333
1.6399 13.02 308 1.7789 0.2308
1.5139 14.02 330 1.6245 0.3590
1.3315 15.02 352 1.6540 0.2821
1.0726 16.02 374 1.7507 0.2821
1.1432 17.02 396 1.6282 0.3333
1.144 18.02 418 1.3435 0.5128
0.987 19.02 440 0.8631 0.7949
0.8152 20.02 462 1.0812 0.5897
0.8175 21.02 484 1.4527 0.4359
0.7587 22.02 506 1.2309 0.5128
0.6255 23.02 528 1.1940 0.4872
0.6867 24.02 550 0.9270 0.5385
0.7537 25.02 572 0.6586 0.7436
0.6147 26.02 594 0.7935 0.7179
0.4602 27.02 616 0.9698 0.6154
0.482 28.02 638 0.9328 0.6410
0.3436 29.02 660 0.9947 0.6154
0.336 30.02 682 0.8127 0.6410
0.3952 31.02 704 0.5542 0.8205
0.2922 32.02 726 1.3266 0.5897
0.2998 33.02 748 0.9621 0.6410
0.2824 34.02 770 0.7805 0.7436
0.2971 35.02 792 0.4700 0.8462
0.1746 36.02 814 0.6059 0.8205
0.1325 37.02 836 0.4568 0.7436
0.2452 38.02 858 0.3495 0.8462
0.161 39.02 880 0.2546 0.9231
0.1788 40.02 902 0.3275 0.8974
0.201 41.02 924 0.3987 0.8205
0.259 42.02 946 0.5395 0.7692
0.112 43.02 968 0.4591 0.8462
0.0622 44.02 990 0.3455 0.8462
0.1307 45.02 1012 0.5513 0.7436
0.0924 46.02 1034 0.6709 0.7436
0.056 47.02 1056 0.4471 0.8205
0.089 48.02 1078 0.3860 0.8205
0.1798 49.02 1100 0.4313 0.8462

Framework versions

  • Transformers 4.40.0
  • Pytorch 2.1.0+cu121
  • Datasets 2.18.0
  • Tokenizers 0.19.1