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videomae-base-finetuned-ucf101-subset

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metadata
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: videomae-base-finetuned-ucf101-subset
    results: []

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: 1.9019
  • Accuracy: 0.5540

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: 64
  • eval_batch_size: 64
  • 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: 1920

Training results

Training Loss Epoch Step Validation Loss Accuracy
4.2451 0.0083 16 4.2284 0.0107
4.2251 1.0083 32 4.2152 0.0107
4.2276 2.0083 48 4.2096 0.0121
4.2146 3.0083 64 4.2124 0.0149
4.2217 4.0083 80 4.2042 0.0178
4.2091 5.0083 96 4.2051 0.0234
4.2085 6.0083 112 4.1939 0.0185
4.2044 7.0083 128 4.1792 0.0391
4.1624 8.0083 144 4.2015 0.0220
4.1253 9.0083 160 4.1215 0.0298
4.0308 10.0083 176 4.0025 0.0710
3.8065 11.0083 192 3.8723 0.0838
3.7614 12.0083 208 3.7139 0.0994
3.4761 13.0083 224 3.6160 0.1435
3.278 14.0083 240 3.3940 0.1925
3.0999 15.0083 256 3.3183 0.2081
2.9721 16.0083 272 3.1596 0.2486
2.8064 17.0083 288 3.0232 0.2670
2.6554 18.0083 304 2.9448 0.2912
2.5052 19.0083 320 2.8285 0.3310
2.4322 20.0083 336 2.7479 0.3402
2.3193 21.0083 352 2.7941 0.3310
2.2565 22.0083 368 2.6383 0.3672
2.1405 23.0083 384 2.5906 0.3608
2.1049 24.0083 400 2.5515 0.3786
1.8424 25.0083 416 2.4692 0.3920
1.8685 26.0083 432 2.4325 0.4276
1.7478 27.0083 448 2.4165 0.4148
1.7072 28.0083 464 2.3617 0.4268
1.7206 29.0083 480 2.3723 0.4304
1.693 30.0083 496 2.2890 0.4425
1.6347 31.0083 512 2.2442 0.4411
1.5276 32.0083 528 2.2104 0.4673
1.4576 33.0083 544 2.2279 0.4545
1.5455 34.0083 560 2.2050 0.4524
1.4485 35.0083 576 2.1585 0.4737
1.3896 36.0083 592 2.1851 0.4446
1.3766 37.0083 608 2.1185 0.4872
1.4035 38.0083 624 2.1164 0.4794
1.5892 39.0083 640 2.1029 0.4801
1.3647 40.0083 656 2.0912 0.4929
1.388 41.0083 672 2.1331 0.4730
1.3425 42.0083 688 2.1437 0.4794
1.2909 43.0083 704 2.1090 0.4716
1.2757 44.0083 720 2.0686 0.4901
1.181 45.0083 736 2.0485 0.4893
1.1825 46.0083 752 2.0561 0.4844
1.1594 47.0083 768 2.0327 0.4964
1.1699 48.0083 784 2.0950 0.4766
1.1908 49.0083 800 2.0465 0.4851
1.1149 50.0083 816 2.0570 0.4879
1.1388 51.0083 832 2.0232 0.4979
1.0421 52.0083 848 2.0133 0.4986
1.1243 53.0083 864 2.0421 0.4901
1.1064 54.0083 880 1.9614 0.5043
0.9778 55.0083 896 1.9939 0.5071
1.1417 56.0083 912 1.9774 0.5107
1.0578 57.0083 928 1.9625 0.5305
1.0904 58.0083 944 1.9713 0.5057
1.2569 59.0083 960 1.9496 0.5256
1.076 60.0083 976 1.9238 0.5369
1.018 61.0083 992 1.9578 0.5156
0.8569 62.0083 1008 1.9410 0.5185
0.9847 63.0083 1024 1.9655 0.5135
0.8992 64.0083 1040 1.9741 0.5185
0.9781 65.0083 1056 1.9591 0.5249
0.9016 66.0083 1072 1.9802 0.5135
0.9443 67.0083 1088 1.9882 0.5036
0.9359 68.0083 1104 2.0046 0.5092
0.7735 69.0083 1120 2.0172 0.5064
0.9405 70.0083 1136 1.9552 0.5270
0.9709 71.0083 1152 1.9573 0.5227
0.9914 72.0083 1168 1.9768 0.5249
0.8487 73.0083 1184 1.9570 0.5327
0.835 74.0083 1200 1.9759 0.5241
0.8914 75.0083 1216 1.9309 0.5298
0.9242 76.0083 1232 1.9595 0.5241
0.8235 77.0083 1248 1.9556 0.5277
0.8664 78.0083 1264 1.9790 0.5135
0.8699 79.0083 1280 1.9835 0.5227
0.9112 80.0083 1296 1.9426 0.5291
0.7901 81.0083 1312 1.9598 0.5256
0.8186 82.0083 1328 1.9397 0.5320
0.8229 83.0083 1344 1.9384 0.5327
0.9063 84.0083 1360 1.9324 0.5291
0.8843 85.0083 1376 1.9316 0.5369
0.7904 86.0083 1392 1.9269 0.5376
0.7942 87.0083 1408 1.9506 0.5291
0.8798 88.0083 1424 1.9185 0.5405
0.7678 89.0083 1440 1.9362 0.5327
0.7589 90.0083 1456 1.9496 0.5277
0.6679 91.0083 1472 1.9507 0.5298
0.8042 92.0083 1488 1.9510 0.5369
0.7722 93.0083 1504 1.9503 0.5334
0.6831 94.0083 1520 1.9531 0.5348
0.766 95.0083 1536 1.9345 0.5384
0.8099 96.0083 1552 1.9349 0.5376
0.7513 97.0083 1568 1.9238 0.5462
0.6561 98.0083 1584 1.9338 0.5426
0.7423 99.0083 1600 1.9019 0.5540
0.7739 100.0083 1616 1.9165 0.5504
0.6562 101.0083 1632 1.9271 0.5433
0.7182 102.0083 1648 1.9096 0.5440
0.6898 103.0083 1664 1.9213 0.5483
0.6541 104.0083 1680 1.9263 0.5433
0.7131 105.0083 1696 1.9148 0.5469
0.7076 106.0083 1712 1.9193 0.5455
0.7822 107.0083 1728 1.9166 0.5440
0.6955 108.0083 1744 1.9167 0.5490
0.6939 109.0083 1760 1.9129 0.5426
0.7149 110.0083 1776 1.9237 0.5355
0.7341 111.0083 1792 1.9047 0.5433
0.7101 112.0083 1808 1.9010 0.5433
0.764 113.0083 1824 1.9024 0.5455
0.667 114.0083 1840 1.9041 0.5476
0.7465 115.0083 1856 1.9006 0.5483
0.6935 116.0083 1872 1.9016 0.5462
0.7306 117.0083 1888 1.9009 0.5483
0.6578 118.0083 1904 1.9008 0.5483
0.6427 119.0083 1920 1.9014 0.5504

Framework versions

  • Transformers 4.48.0
  • Pytorch 2.5.1+cu118
  • Datasets 3.2.0
  • Tokenizers 0.21.0