videomae-base-finetuned-signlanguage

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: 1.3358
  • Accuracy: 0.6567

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: 1e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • 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: 3560

Training results

Training Loss Epoch Step Validation Loss Accuracy
4.2372 0.0065 23 4.2439 0.0186
4.2325 1.0065 46 4.2369 0.0186
4.1795 2.0065 69 4.2250 0.0233
4.211 3.0065 92 4.2141 0.0140
4.2361 4.0065 115 4.2051 0.0372
4.2524 5.0065 138 4.2052 0.0233
4.2297 6.0065 161 4.2014 0.0186
4.2596 7.0065 184 4.1950 0.0233
4.1911 8.0065 207 4.1898 0.0326
4.184 9.0065 230 4.1860 0.0279
4.1731 10.0065 253 4.1680 0.0419
4.098 11.0065 276 4.1413 0.0512
4.1105 12.0065 299 4.1351 0.0279
4.1477 13.0065 322 4.1182 0.0372
4.0121 14.0065 345 4.0324 0.0605
3.8587 15.0065 368 3.9973 0.0512
3.8878 16.0065 391 3.9077 0.0651
3.7205 17.0065 414 3.8901 0.0698
3.6613 18.0065 437 3.7347 0.1349
3.5438 19.0065 460 3.6275 0.1488
3.4033 20.0065 483 3.5495 0.1442
3.2043 21.0065 506 3.5864 0.1349
3.1477 22.0065 529 3.4515 0.1674
3.0344 23.0065 552 3.3110 0.2233
2.9459 24.0065 575 3.2645 0.2605
2.6629 25.0065 598 3.1746 0.2558
2.764 26.0065 621 3.0833 0.3163
2.4924 27.0065 644 2.9918 0.3023
2.6696 28.0065 667 3.0009 0.3349
2.4616 29.0065 690 2.8396 0.4
2.2084 30.0065 713 2.8039 0.3674
2.3011 31.0065 736 2.7465 0.4
2.1059 32.0065 759 2.6865 0.4140
2.0525 33.0065 782 2.6070 0.4326
2.1054 34.0065 805 2.6387 0.3953
1.8791 35.0065 828 2.5539 0.4326
1.7834 36.0065 851 2.4750 0.4326
1.5749 37.0065 874 2.4880 0.4233
1.6162 38.0065 897 2.3581 0.4884
1.5611 39.0065 920 2.2846 0.5256
1.5449 40.0065 943 2.2999 0.5116
1.6013 41.0065 966 2.2349 0.5302
1.3959 42.0065 989 2.1957 0.5488
1.1607 43.0065 1012 2.1559 0.5209
1.2663 44.0065 1035 2.1192 0.5721
1.0869 45.0065 1058 2.0522 0.5535
1.2477 46.0065 1081 2.0597 0.5767
0.9665 47.0065 1104 2.0365 0.5535
1.0521 48.0065 1127 1.9669 0.5767
0.8273 49.0065 1150 1.9918 0.5907
0.8396 50.0065 1173 1.9576 0.5860
0.9543 51.0065 1196 1.9371 0.5953
0.8095 52.0065 1219 1.8800 0.5953
0.7694 53.0065 1242 1.8737 0.6
0.8243 54.0065 1265 1.8846 0.6093
0.6632 55.0065 1288 1.8230 0.6
0.7446 56.0065 1311 1.7898 0.6093
0.7044 57.0065 1334 1.7740 0.5907
0.6732 58.0065 1357 1.8061 0.6047
0.5786 59.0065 1380 1.7060 0.6186
0.6348 60.0065 1403 1.7004 0.6140
0.5706 61.0065 1426 1.7013 0.6279
0.5007 62.0065 1449 1.6992 0.6186
0.5078 63.0065 1472 1.6649 0.6047
0.5048 64.0065 1495 1.6449 0.6140
0.4526 65.0065 1518 1.6256 0.6279
0.504 66.0065 1541 1.6401 0.6372
0.3824 67.0065 1564 1.5941 0.6093
0.453 68.0065 1587 1.6236 0.6186
0.3618 69.0065 1610 1.6100 0.6186
0.3689 70.0065 1633 1.5488 0.6419
0.3545 71.0065 1656 1.5390 0.6465
0.4126 72.0065 1679 1.5287 0.6558
0.2734 73.0065 1702 1.4978 0.6465
0.3144 74.0065 1725 1.5038 0.6326
0.3152 75.0065 1748 1.5692 0.6326
0.371 76.0065 1771 1.5331 0.6558
0.3033 77.0065 1794 1.4733 0.6465
0.2574 78.0065 1817 1.5694 0.5907
0.2562 79.0065 1840 1.5097 0.6279
0.2162 80.0065 1863 1.4782 0.6512
0.2493 81.0065 1886 1.4350 0.6465
0.2173 82.0065 1909 1.4730 0.6093
0.2508 83.0065 1932 1.4735 0.6186
0.1932 84.0065 1955 1.4491 0.6326
0.1822 85.0065 1978 1.4155 0.6326
0.2051 86.0065 2001 1.4431 0.6419
0.2269 87.0065 2024 1.4029 0.6419
0.1747 88.0065 2047 1.4643 0.6233
0.1464 89.0065 2070 1.3921 0.6558
0.1642 90.0065 2093 1.4033 0.6512
0.1582 91.0065 2116 1.3728 0.6512
0.1641 92.0065 2139 1.3756 0.6326
0.1292 93.0065 2162 1.3731 0.6512
0.1285 94.0065 2185 1.3559 0.6698
0.1405 95.0065 2208 1.4126 0.6233
0.1299 96.0065 2231 1.3524 0.6419
0.1166 97.0065 2254 1.3812 0.6512
0.1434 98.0065 2277 1.4055 0.6279
0.1748 99.0065 2300 1.3894 0.6558
0.0999 100.0065 2323 1.3665 0.6326
0.1361 101.0065 2346 1.3776 0.6372
0.118 102.0065 2369 1.3635 0.6558
0.0996 103.0065 2392 1.3477 0.6512
0.1232 104.0065 2415 1.3550 0.6419
0.0783 105.0065 2438 1.3460 0.6233
0.1517 106.0065 2461 1.3527 0.6279
0.1007 107.0065 2484 1.3040 0.6465
0.1036 108.0065 2507 1.3216 0.6698
0.1085 109.0065 2530 1.2975 0.6326
0.0691 110.0065 2553 1.3401 0.6512
0.1231 111.0065 2576 1.3251 0.6372
0.0801 112.0065 2599 1.3120 0.6605
0.0784 113.0065 2622 1.3061 0.6605
0.0891 114.0065 2645 1.2882 0.6558
0.0792 115.0065 2668 1.3531 0.6558
0.0772 116.0065 2691 1.3200 0.6698
0.1068 117.0065 2714 1.3186 0.6744
0.0711 118.0065 2737 1.3067 0.6419
0.0982 119.0065 2760 1.3161 0.6512
0.0741 120.0065 2783 1.3029 0.6512
0.1507 121.0065 2806 1.3406 0.6605
0.0602 122.0065 2829 1.3187 0.6558
0.0748 123.0065 2852 1.2874 0.6605
0.0638 124.0065 2875 1.2871 0.6791
0.0915 125.0065 2898 1.2869 0.6465
0.0749 126.0065 2921 1.2859 0.6558
0.0717 127.0065 2944 1.3222 0.6372
0.0539 128.0065 2967 1.3263 0.6326
0.0488 129.0065 2990 1.2945 0.6512
0.0696 130.0065 3013 1.2636 0.6698
0.0665 131.0065 3036 1.2910 0.6698
0.0562 132.0065 3059 1.2820 0.6558
0.0527 133.0065 3082 1.2927 0.6651
0.057 134.0065 3105 1.2846 0.6558
0.0764 135.0065 3128 1.3104 0.6651
0.0805 136.0065 3151 1.3110 0.6465
0.0503 137.0065 3174 1.3107 0.6558
0.0629 138.0065 3197 1.2915 0.6465
0.0491 139.0065 3220 1.2753 0.6558
0.0456 140.0065 3243 1.3105 0.6605
0.053 141.0065 3266 1.2686 0.6558
0.051 142.0065 3289 1.2831 0.6651
0.0669 143.0065 3312 1.2852 0.6698
0.0445 144.0065 3335 1.2868 0.6651
0.0414 145.0065 3358 1.2806 0.6698
0.0575 146.0065 3381 1.2815 0.6651
0.0392 147.0065 3404 1.2596 0.6558
0.0898 148.0065 3427 1.2666 0.6698
0.0617 149.0065 3450 1.2629 0.6605
0.0453 150.0065 3473 1.2797 0.6651
0.0398 151.0065 3496 1.2698 0.6558
0.0435 152.0065 3519 1.2721 0.6558
0.0507 153.0065 3542 1.2745 0.6512
0.0525 154.0051 3560 1.2777 0.6512

Framework versions

  • Transformers 4.49.0
  • Pytorch 2.0.1+cu118
  • Datasets 3.3.2
  • Tokenizers 0.21.0
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