--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-large-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: MAE-CT-M1N0-M12_v8_split4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MAE-CT-M1N0-M12_v8_split4 This model is a fine-tuned version of [MCG-NJU/videomae-large-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-large-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4312 - Accuracy: 0.8267 ## 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: 4 - eval_batch_size: 4 - 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: 6400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.6773 | 0.0102 | 65 | 0.7109 | 0.4348 | | 0.7393 | 1.0102 | 130 | 0.7720 | 0.4348 | | 0.6483 | 2.0102 | 195 | 0.8131 | 0.4348 | | 0.5872 | 3.0102 | 260 | 0.7178 | 0.4348 | | 0.5612 | 4.0102 | 325 | 0.6203 | 0.6957 | | 0.2855 | 5.0102 | 390 | 0.7647 | 0.3913 | | 0.3332 | 6.0102 | 455 | 0.9563 | 0.3913 | | 0.5376 | 7.0102 | 520 | 1.0380 | 0.4348 | | 0.3236 | 8.0102 | 585 | 0.6013 | 0.7826 | | 0.2583 | 9.0102 | 650 | 0.6642 | 0.6957 | | 0.519 | 10.0102 | 715 | 0.8797 | 0.6522 | | 0.2594 | 11.0102 | 780 | 0.8123 | 0.7391 | | 0.2015 | 12.0102 | 845 | 1.2630 | 0.6522 | | 0.3333 | 13.0102 | 910 | 1.4962 | 0.6087 | | 0.1593 | 14.0102 | 975 | 1.1972 | 0.6957 | | 0.1296 | 15.0102 | 1040 | 1.1893 | 0.7826 | | 0.3097 | 16.0102 | 1105 | 1.5245 | 0.7391 | | 0.1145 | 17.0102 | 1170 | 1.2979 | 0.7826 | | 0.2288 | 18.0102 | 1235 | 1.7658 | 0.6957 | | 0.0217 | 19.0102 | 1300 | 2.6377 | 0.6087 | | 0.1368 | 20.0102 | 1365 | 1.6947 | 0.6957 | | 0.1717 | 21.0102 | 1430 | 1.8905 | 0.6522 | | 0.0014 | 22.0102 | 1495 | 2.1503 | 0.6522 | | 0.012 | 23.0102 | 1560 | 2.0506 | 0.6522 | | 0.0007 | 24.0102 | 1625 | 2.3373 | 0.6522 | | 0.0001 | 25.0102 | 1690 | 1.6162 | 0.7391 | | 0.0002 | 26.0102 | 1755 | 2.7662 | 0.6087 | | 0.104 | 27.0102 | 1820 | 1.5637 | 0.7826 | | 0.1848 | 28.0102 | 1885 | 3.6887 | 0.5217 | | 0.0015 | 29.0102 | 1950 | 1.7133 | 0.6957 | | 0.0001 | 30.0102 | 2015 | 2.1864 | 0.7391 | | 0.0008 | 31.0102 | 2080 | 1.9452 | 0.7391 | | 0.0002 | 32.0102 | 2145 | 1.7982 | 0.7391 | | 0.0001 | 33.0102 | 2210 | 2.3272 | 0.6957 | | 0.0072 | 34.0102 | 2275 | 2.5865 | 0.6957 | | 0.275 | 35.0102 | 2340 | 4.0065 | 0.5652 | | 0.0004 | 36.0102 | 2405 | 1.4350 | 0.7826 | | 0.0001 | 37.0102 | 2470 | 1.8396 | 0.7826 | | 0.1562 | 38.0102 | 2535 | 2.6788 | 0.6522 | | 0.0001 | 39.0102 | 2600 | 2.0010 | 0.6957 | | 0.0001 | 40.0102 | 2665 | 2.4220 | 0.6522 | | 0.1117 | 41.0102 | 2730 | 2.3290 | 0.6957 | | 0.0001 | 42.0102 | 2795 | 3.1235 | 0.5652 | | 0.0001 | 43.0102 | 2860 | 2.9064 | 0.6087 | | 0.0003 | 44.0102 | 2925 | 3.1359 | 0.6087 | | 0.0007 | 45.0102 | 2990 | 3.1225 | 0.6087 | | 0.0031 | 46.0102 | 3055 | 2.9252 | 0.6087 | | 0.0 | 47.0102 | 3120 | 3.3919 | 0.5652 | | 0.0003 | 48.0102 | 3185 | 2.8240 | 0.6957 | | 0.0014 | 49.0102 | 3250 | 2.4431 | 0.5652 | | 0.0001 | 50.0102 | 3315 | 2.2488 | 0.6957 | | 0.0 | 51.0102 | 3380 | 2.6169 | 0.6087 | | 0.0 | 52.0102 | 3445 | 2.4118 | 0.7391 | | 0.0002 | 53.0102 | 3510 | 2.4928 | 0.5652 | | 0.0001 | 54.0102 | 3575 | 3.6149 | 0.5652 | | 0.0 | 55.0102 | 3640 | 3.2978 | 0.5652 | | 0.0 | 56.0102 | 3705 | 2.9060 | 0.5217 | | 0.1108 | 57.0102 | 3770 | 3.0361 | 0.6087 | | 0.0 | 58.0102 | 3835 | 3.3929 | 0.6087 | | 0.0 | 59.0102 | 3900 | 3.5174 | 0.5652 | | 0.0007 | 60.0102 | 3965 | 2.1117 | 0.7391 | | 0.0 | 61.0102 | 4030 | 3.5274 | 0.6087 | | 0.0 | 62.0102 | 4095 | 3.5149 | 0.6087 | | 0.0 | 63.0102 | 4160 | 3.4865 | 0.6087 | | 0.0 | 64.0102 | 4225 | 3.2318 | 0.6087 | | 0.0 | 65.0102 | 4290 | 3.1844 | 0.6087 | | 0.0 | 66.0102 | 4355 | 3.2181 | 0.6087 | | 0.0 | 67.0102 | 4420 | 3.2936 | 0.6087 | | 0.0 | 68.0102 | 4485 | 3.3043 | 0.6087 | | 0.0 | 69.0102 | 4550 | 3.1360 | 0.6522 | | 0.0186 | 70.0102 | 4615 | 2.3659 | 0.7391 | | 0.0 | 71.0102 | 4680 | 2.5226 | 0.7391 | | 0.0 | 72.0102 | 4745 | 2.7737 | 0.6522 | | 0.0 | 73.0102 | 4810 | 2.6730 | 0.6957 | | 0.0 | 74.0102 | 4875 | 2.7865 | 0.6957 | | 0.0 | 75.0102 | 4940 | 2.7922 | 0.6957 | | 0.0 | 76.0102 | 5005 | 3.0552 | 0.6087 | | 0.0 | 77.0102 | 5070 | 2.4933 | 0.7391 | | 0.0044 | 78.0102 | 5135 | 2.1811 | 0.7391 | | 0.0 | 79.0102 | 5200 | 1.9051 | 0.7826 | | 0.0 | 80.0102 | 5265 | 1.8407 | 0.8261 | | 0.0 | 81.0102 | 5330 | 2.1967 | 0.7826 | | 0.0 | 82.0102 | 5395 | 2.3231 | 0.6957 | | 0.0 | 83.0102 | 5460 | 2.3425 | 0.6957 | | 0.0 | 84.0102 | 5525 | 2.8403 | 0.5652 | | 0.0 | 85.0102 | 5590 | 2.3424 | 0.6957 | | 0.0 | 86.0102 | 5655 | 2.4246 | 0.6957 | | 0.0 | 87.0102 | 5720 | 2.4289 | 0.6957 | | 0.0 | 88.0102 | 5785 | 2.4310 | 0.6957 | | 0.0 | 89.0102 | 5850 | 2.4361 | 0.6957 | | 0.0 | 90.0102 | 5915 | 2.3667 | 0.6957 | | 0.0 | 91.0102 | 5980 | 2.3627 | 0.6957 | | 0.0 | 92.0102 | 6045 | 2.3715 | 0.6957 | | 0.0 | 93.0102 | 6110 | 2.3773 | 0.7391 | | 0.0 | 94.0102 | 6175 | 2.4264 | 0.7391 | | 0.0 | 95.0102 | 6240 | 2.4393 | 0.7391 | | 0.0 | 96.0102 | 6305 | 2.4449 | 0.7391 | | 0.0 | 97.0102 | 6370 | 2.4451 | 0.7391 | | 0.0 | 98.0047 | 6400 | 2.4451 | 0.7391 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.0.1+cu117 - Datasets 3.0.1 - Tokenizers 0.20.0