--- 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](https://huggingface.co/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