--- 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_split1_v3 results: [] --- # MAE-CT-M1N0-M12_v8_split1_v3 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: 0.4942 - Accuracy: 0.7838 ## 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: 10500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:--------:|:-----:|:---------------:|:--------:| | 0.6862 | 0.0068 | 71 | 0.6571 | 0.6622 | | 0.665 | 1.0068 | 142 | 0.6370 | 0.6622 | | 0.7033 | 2.0068 | 213 | 0.6254 | 0.6622 | | 0.6524 | 3.0068 | 284 | 0.6091 | 0.6757 | | 0.5611 | 4.0068 | 355 | 0.5565 | 0.6622 | | 0.4274 | 5.0068 | 426 | 0.5154 | 0.7162 | | 0.4797 | 6.0068 | 497 | 0.5644 | 0.6757 | | 0.3758 | 7.0068 | 568 | 0.4942 | 0.7838 | | 0.4243 | 8.0068 | 639 | 0.5252 | 0.7568 | | 0.5133 | 9.0068 | 710 | 0.6873 | 0.6757 | | 0.3709 | 10.0068 | 781 | 0.6555 | 0.7568 | | 0.2793 | 11.0068 | 852 | 0.7140 | 0.7568 | | 0.6153 | 12.0068 | 923 | 1.3006 | 0.6892 | | 0.7185 | 13.0068 | 994 | 1.6663 | 0.6892 | | 0.4609 | 14.0068 | 1065 | 1.3522 | 0.7162 | | 0.236 | 15.0068 | 1136 | 1.2228 | 0.7297 | | 0.0519 | 16.0068 | 1207 | 1.0973 | 0.7568 | | 0.0026 | 17.0068 | 1278 | 1.4476 | 0.7432 | | 0.357 | 18.0068 | 1349 | 1.4487 | 0.7432 | | 0.4262 | 19.0068 | 1420 | 1.1604 | 0.7838 | | 0.0021 | 20.0068 | 1491 | 1.7720 | 0.7027 | | 0.0132 | 21.0068 | 1562 | 1.7388 | 0.7297 | | 0.1451 | 22.0068 | 1633 | 1.7954 | 0.6892 | | 0.0099 | 23.0068 | 1704 | 2.1619 | 0.7162 | | 0.0001 | 24.0068 | 1775 | 1.6524 | 0.7297 | | 0.0005 | 25.0068 | 1846 | 1.8499 | 0.7162 | | 0.0388 | 26.0068 | 1917 | 1.8792 | 0.7027 | | 0.1798 | 27.0068 | 1988 | 1.2951 | 0.7568 | | 0.2354 | 28.0068 | 2059 | 1.5408 | 0.7297 | | 0.0024 | 29.0068 | 2130 | 1.9224 | 0.7162 | | 0.0018 | 30.0068 | 2201 | 2.5244 | 0.6486 | | 0.1072 | 31.0068 | 2272 | 2.8444 | 0.6486 | | 0.0664 | 32.0068 | 2343 | 1.8277 | 0.7297 | | 0.0122 | 33.0068 | 2414 | 2.1148 | 0.7297 | | 0.1118 | 34.0068 | 2485 | 1.5536 | 0.7703 | | 0.1987 | 35.0068 | 2556 | 2.2923 | 0.7027 | | 0.0012 | 36.0068 | 2627 | 2.6785 | 0.6622 | | 0.0027 | 37.0068 | 2698 | 2.2400 | 0.6757 | | 0.0002 | 38.0068 | 2769 | 2.2459 | 0.7162 | | 0.0099 | 39.0068 | 2840 | 2.3601 | 0.6622 | | 0.0071 | 40.0068 | 2911 | 2.0561 | 0.7297 | | 0.0086 | 41.0068 | 2982 | 2.1898 | 0.7027 | | 0.0131 | 42.0068 | 3053 | 2.6086 | 0.6757 | | 0.0002 | 43.0068 | 3124 | 2.1400 | 0.6892 | | 0.0001 | 44.0068 | 3195 | 2.2608 | 0.7027 | | 0.0549 | 45.0068 | 3266 | 2.0129 | 0.7432 | | 0.0 | 46.0068 | 3337 | 2.0018 | 0.7297 | | 0.0001 | 47.0068 | 3408 | 1.7209 | 0.7838 | | 0.31 | 48.0068 | 3479 | 2.1962 | 0.7027 | | 0.0001 | 49.0068 | 3550 | 1.6650 | 0.7568 | | 0.0 | 50.0068 | 3621 | 1.8843 | 0.7568 | | 0.0 | 51.0068 | 3692 | 1.9398 | 0.7703 | | 0.0 | 52.0068 | 3763 | 1.7851 | 0.7568 | | 0.0001 | 53.0068 | 3834 | 1.9574 | 0.7162 | | 0.0002 | 54.0068 | 3905 | 2.6200 | 0.6351 | | 0.0 | 55.0068 | 3976 | 2.2333 | 0.7027 | | 0.0001 | 56.0068 | 4047 | 2.7799 | 0.6757 | | 0.0001 | 57.0068 | 4118 | 2.1935 | 0.7027 | | 0.0188 | 58.0068 | 4189 | 2.2272 | 0.7162 | | 0.1013 | 59.0068 | 4260 | 2.3607 | 0.7027 | | 0.0001 | 60.0068 | 4331 | 2.1223 | 0.7432 | | 0.0026 | 61.0068 | 4402 | 1.9220 | 0.7568 | | 0.193 | 62.0068 | 4473 | 2.2254 | 0.7027 | | 0.0002 | 63.0068 | 4544 | 2.2682 | 0.6622 | | 0.0 | 64.0068 | 4615 | 2.6857 | 0.6892 | | 0.0 | 65.0068 | 4686 | 2.3791 | 0.7297 | | 0.0076 | 66.0068 | 4757 | 2.8393 | 0.6757 | | 0.0043 | 67.0068 | 4828 | 1.9305 | 0.7162 | | 0.0003 | 68.0068 | 4899 | 1.9944 | 0.7297 | | 0.0 | 69.0068 | 4970 | 2.5842 | 0.7162 | | 0.0001 | 70.0068 | 5041 | 2.6503 | 0.6622 | | 0.0 | 71.0068 | 5112 | 2.7254 | 0.6757 | | 0.0002 | 72.0068 | 5183 | 3.0429 | 0.6622 | | 0.0 | 73.0068 | 5254 | 2.5716 | 0.7027 | | 0.0671 | 74.0068 | 5325 | 2.5144 | 0.7027 | | 0.0 | 75.0068 | 5396 | 2.8938 | 0.6622 | | 0.0 | 76.0068 | 5467 | 2.8503 | 0.6622 | | 0.0 | 77.0068 | 5538 | 2.8861 | 0.6622 | | 0.0 | 78.0068 | 5609 | 2.8524 | 0.6892 | | 0.0 | 79.0068 | 5680 | 2.7962 | 0.6757 | | 0.0 | 80.0068 | 5751 | 2.8640 | 0.6622 | | 0.0 | 81.0068 | 5822 | 2.8446 | 0.6757 | | 0.0 | 82.0068 | 5893 | 2.6401 | 0.6892 | | 0.0007 | 83.0068 | 5964 | 2.3987 | 0.7432 | | 0.0 | 84.0068 | 6035 | 2.3642 | 0.7162 | | 0.0 | 85.0068 | 6106 | 2.4710 | 0.6757 | | 0.0004 | 86.0068 | 6177 | 3.0323 | 0.6486 | | 0.0 | 87.0068 | 6248 | 3.0862 | 0.6351 | | 0.2299 | 88.0068 | 6319 | 2.0283 | 0.7703 | | 0.0 | 89.0068 | 6390 | 2.3752 | 0.6892 | | 0.1842 | 90.0068 | 6461 | 2.2107 | 0.7568 | | 0.0002 | 91.0068 | 6532 | 3.1361 | 0.6622 | | 0.0 | 92.0068 | 6603 | 2.7366 | 0.7027 | | 0.0 | 93.0068 | 6674 | 2.6850 | 0.6892 | | 0.0 | 94.0068 | 6745 | 2.6965 | 0.6892 | | 0.1894 | 95.0068 | 6816 | 2.5070 | 0.7162 | | 0.0 | 96.0068 | 6887 | 2.5327 | 0.7162 | | 0.0 | 97.0068 | 6958 | 2.8845 | 0.6892 | | 0.0 | 98.0068 | 7029 | 2.0030 | 0.7838 | | 0.1439 | 99.0068 | 7100 | 2.7892 | 0.6892 | | 0.0 | 100.0068 | 7171 | 2.4622 | 0.7162 | | 0.0016 | 101.0068 | 7242 | 2.4540 | 0.7297 | | 0.0006 | 102.0068 | 7313 | 2.4853 | 0.7162 | | 0.0 | 103.0068 | 7384 | 2.5101 | 0.7297 | | 0.0 | 104.0068 | 7455 | 2.5136 | 0.7162 | | 0.0 | 105.0068 | 7526 | 2.5028 | 0.7297 | | 0.0039 | 106.0068 | 7597 | 2.6882 | 0.7162 | | 0.0 | 107.0068 | 7668 | 2.8377 | 0.7162 | | 0.0 | 108.0068 | 7739 | 2.8495 | 0.7162 | | 0.0073 | 109.0068 | 7810 | 2.6625 | 0.7297 | | 0.0 | 110.0068 | 7881 | 2.7063 | 0.7162 | | 0.0 | 111.0068 | 7952 | 2.3949 | 0.7568 | | 0.0 | 112.0068 | 8023 | 2.5956 | 0.7432 | | 0.0001 | 113.0068 | 8094 | 2.9212 | 0.7027 | | 0.0 | 114.0068 | 8165 | 2.8216 | 0.6892 | | 0.0 | 115.0068 | 8236 | 2.8409 | 0.6892 | | 0.0 | 116.0068 | 8307 | 2.8546 | 0.6892 | | 0.0 | 117.0068 | 8378 | 2.8172 | 0.6892 | | 0.0 | 118.0068 | 8449 | 2.4546 | 0.7432 | | 0.0 | 119.0068 | 8520 | 2.3815 | 0.7568 | | 0.0 | 120.0068 | 8591 | 2.4006 | 0.7432 | | 0.0 | 121.0068 | 8662 | 2.4198 | 0.7432 | | 0.0 | 122.0068 | 8733 | 2.4389 | 0.7432 | | 0.0 | 123.0068 | 8804 | 2.4763 | 0.7432 | | 0.0 | 124.0068 | 8875 | 2.4947 | 0.7432 | | 0.0 | 125.0068 | 8946 | 2.5126 | 0.7297 | | 0.0 | 126.0068 | 9017 | 2.5314 | 0.7297 | | 0.0 | 127.0068 | 9088 | 2.5429 | 0.7297 | | 0.0 | 128.0068 | 9159 | 2.5660 | 0.7297 | | 0.0 | 129.0068 | 9230 | 2.5828 | 0.7162 | | 0.0 | 130.0068 | 9301 | 2.5996 | 0.7162 | | 0.0 | 131.0068 | 9372 | 2.6081 | 0.7162 | | 0.0 | 132.0068 | 9443 | 2.6265 | 0.7162 | | 0.0 | 133.0068 | 9514 | 2.6524 | 0.7162 | | 0.0 | 134.0068 | 9585 | 2.6634 | 0.7162 | | 0.0 | 135.0068 | 9656 | 2.6925 | 0.7162 | | 0.0 | 136.0068 | 9727 | 2.7701 | 0.7162 | | 0.0 | 137.0068 | 9798 | 2.7774 | 0.7027 | | 0.0 | 138.0068 | 9869 | 2.7756 | 0.7162 | | 0.0 | 139.0068 | 9940 | 2.7789 | 0.7162 | | 0.0 | 140.0068 | 10011 | 2.7818 | 0.7162 | | 0.0 | 141.0068 | 10082 | 2.7164 | 0.7162 | | 0.0 | 142.0068 | 10153 | 2.9571 | 0.7027 | | 0.0 | 143.0068 | 10224 | 2.9562 | 0.7027 | | 0.0 | 144.0068 | 10295 | 2.9538 | 0.7027 | | 0.0 | 145.0068 | 10366 | 2.9514 | 0.7027 | | 0.0 | 146.0068 | 10437 | 2.9517 | 0.7027 | | 0.0 | 147.006 | 10500 | 2.9518 | 0.7027 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.0.1+cu117 - Datasets 3.0.1 - Tokenizers 0.20.0