--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: MAE-CT-M1N0-M12_v8_split1_v3 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_split1_v3 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: 0.3263 - Accuracy: 0.8696 ## 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 | |:-------------:|:--------:|:-----:|:---------------:|:--------:| | 0.6778 | 0.0068 | 71 | 0.6620 | 0.6622 | | 0.6974 | 1.0068 | 142 | 0.6518 | 0.6622 | | 0.7123 | 2.0068 | 213 | 0.6538 | 0.6622 | | 0.6797 | 3.0068 | 284 | 0.6663 | 0.6757 | | 0.6391 | 4.0068 | 355 | 0.6381 | 0.6622 | | 0.643 | 5.0068 | 426 | 0.6440 | 0.6622 | | 0.6763 | 6.0068 | 497 | 0.6331 | 0.6622 | | 0.6547 | 7.0068 | 568 | 0.6475 | 0.6622 | | 0.6751 | 8.0068 | 639 | 0.6370 | 0.6622 | | 0.6847 | 9.0068 | 710 | 0.6344 | 0.6622 | | 0.7185 | 10.0068 | 781 | 0.6262 | 0.6622 | | 0.6961 | 11.0068 | 852 | 0.6510 | 0.6622 | | 0.6824 | 12.0068 | 923 | 0.6236 | 0.7162 | | 0.6169 | 13.0068 | 994 | 0.6485 | 0.6622 | | 0.6172 | 14.0068 | 1065 | 0.5578 | 0.6622 | | 0.6671 | 15.0068 | 1136 | 0.5988 | 0.6486 | | 0.6063 | 16.0068 | 1207 | 0.5371 | 0.7027 | | 0.4294 | 17.0068 | 1278 | 0.9391 | 0.6622 | | 0.5702 | 18.0068 | 1349 | 0.5392 | 0.6757 | | 0.5217 | 19.0068 | 1420 | 0.5673 | 0.6892 | | 0.4067 | 20.0068 | 1491 | 0.6192 | 0.6892 | | 0.2278 | 21.0068 | 1562 | 0.8934 | 0.6622 | | 0.7341 | 22.0068 | 1633 | 0.6416 | 0.7027 | | 0.4694 | 23.0068 | 1704 | 0.4830 | 0.7297 | | 0.4655 | 24.0068 | 1775 | 0.8866 | 0.6757 | | 0.433 | 25.0068 | 1846 | 0.8913 | 0.7568 | | 0.4986 | 26.0068 | 1917 | 1.0156 | 0.7027 | | 0.4063 | 27.0068 | 1988 | 1.1915 | 0.6892 | | 0.3722 | 28.0068 | 2059 | 1.3529 | 0.6892 | | 0.2947 | 29.0068 | 2130 | 1.6801 | 0.6351 | | 0.1906 | 30.0068 | 2201 | 0.9845 | 0.6892 | | 0.0161 | 31.0068 | 2272 | 1.0789 | 0.7027 | | 0.5682 | 32.0068 | 2343 | 1.2568 | 0.7162 | | 0.1105 | 33.0068 | 2414 | 1.0929 | 0.7432 | | 0.1818 | 34.0068 | 2485 | 1.1917 | 0.7027 | | 0.5396 | 35.0068 | 2556 | 1.4710 | 0.6892 | | 0.0868 | 36.0068 | 2627 | 1.5799 | 0.7297 | | 0.2748 | 37.0068 | 2698 | 1.3387 | 0.6892 | | 0.1488 | 38.0068 | 2769 | 1.4294 | 0.6892 | | 0.3124 | 39.0068 | 2840 | 1.1473 | 0.7027 | | 0.1499 | 40.0068 | 2911 | 1.8165 | 0.6757 | | 0.3149 | 41.0068 | 2982 | 2.0903 | 0.6351 | | 0.02 | 42.0068 | 3053 | 1.9185 | 0.7027 | | 0.0852 | 43.0068 | 3124 | 1.4491 | 0.6892 | | 0.0115 | 44.0068 | 3195 | 1.6180 | 0.7297 | | 0.5243 | 45.0068 | 3266 | 1.8516 | 0.6892 | | 0.0658 | 46.0068 | 3337 | 1.6331 | 0.7027 | | 0.1269 | 47.0068 | 3408 | 2.0585 | 0.6892 | | 0.2941 | 48.0068 | 3479 | 2.1071 | 0.6892 | | 0.2149 | 49.0068 | 3550 | 1.4238 | 0.7162 | | 0.0017 | 50.0068 | 3621 | 1.6924 | 0.7297 | | 0.0004 | 51.0068 | 3692 | 1.7705 | 0.7162 | | 0.6701 | 52.0068 | 3763 | 2.1679 | 0.6892 | | 0.5874 | 53.0068 | 3834 | 1.8656 | 0.6351 | | 0.0004 | 54.0068 | 3905 | 2.1886 | 0.6622 | | 0.0183 | 55.0068 | 3976 | 2.0148 | 0.6486 | | 0.0056 | 56.0068 | 4047 | 1.9963 | 0.6892 | | 0.0014 | 57.0068 | 4118 | 1.9338 | 0.7162 | | 0.2153 | 58.0068 | 4189 | 1.6661 | 0.7297 | | 0.0003 | 59.0068 | 4260 | 1.9540 | 0.7162 | | 0.3193 | 60.0068 | 4331 | 2.1075 | 0.7027 | | 0.0004 | 61.0068 | 4402 | 1.5376 | 0.7432 | | 0.0003 | 62.0068 | 4473 | 1.9647 | 0.7027 | | 0.0006 | 63.0068 | 4544 | 1.8878 | 0.7297 | | 0.0018 | 64.0068 | 4615 | 1.7761 | 0.7297 | | 0.0002 | 65.0068 | 4686 | 1.7536 | 0.7027 | | 0.0001 | 66.0068 | 4757 | 2.2684 | 0.6757 | | 0.0002 | 67.0068 | 4828 | 1.7061 | 0.7162 | | 0.0498 | 68.0068 | 4899 | 1.8082 | 0.7162 | | 0.0007 | 69.0068 | 4970 | 1.7665 | 0.7297 | | 0.0019 | 70.0068 | 5041 | 2.5360 | 0.6757 | | 0.0854 | 71.0068 | 5112 | 2.0176 | 0.6892 | | 0.153 | 72.0068 | 5183 | 2.6058 | 0.6351 | | 0.0001 | 73.0068 | 5254 | 1.9414 | 0.7162 | | 0.1577 | 74.0068 | 5325 | 2.1872 | 0.6892 | | 0.0001 | 75.0068 | 5396 | 1.9070 | 0.7027 | | 0.0001 | 76.0068 | 5467 | 2.1586 | 0.7027 | | 0.0001 | 77.0068 | 5538 | 2.4877 | 0.6757 | | 0.0001 | 78.0068 | 5609 | 2.1836 | 0.7297 | | 0.0021 | 79.0068 | 5680 | 2.6697 | 0.6622 | | 0.0001 | 80.0068 | 5751 | 1.8825 | 0.7432 | | 0.0004 | 81.0068 | 5822 | 2.1590 | 0.6892 | | 0.0003 | 82.0068 | 5893 | 1.8814 | 0.7568 | | 0.0118 | 83.0068 | 5964 | 1.8479 | 0.7027 | | 0.1773 | 84.0068 | 6035 | 1.6983 | 0.7297 | | 0.0025 | 85.0068 | 6106 | 2.5502 | 0.6351 | | 0.0001 | 86.0068 | 6177 | 2.2446 | 0.7027 | | 0.0001 | 87.0068 | 6248 | 2.0950 | 0.7162 | | 0.0001 | 88.0068 | 6319 | 2.2134 | 0.7162 | | 0.0001 | 89.0068 | 6390 | 1.9576 | 0.7432 | | 0.0001 | 90.0068 | 6461 | 2.0430 | 0.7027 | | 0.0001 | 91.0068 | 6532 | 2.1319 | 0.7297 | | 0.0034 | 92.0068 | 6603 | 2.4718 | 0.6892 | | 0.0001 | 93.0068 | 6674 | 2.5268 | 0.6892 | | 0.0001 | 94.0068 | 6745 | 2.4211 | 0.7027 | | 0.0001 | 95.0068 | 6816 | 2.3971 | 0.6892 | | 0.1517 | 96.0068 | 6887 | 2.2035 | 0.7297 | | 0.0001 | 97.0068 | 6958 | 2.3758 | 0.6757 | | 0.0001 | 98.0068 | 7029 | 2.2253 | 0.7162 | | 0.0001 | 99.0068 | 7100 | 2.3226 | 0.7162 | | 0.0001 | 100.0068 | 7171 | 2.2541 | 0.7297 | | 0.0 | 101.0068 | 7242 | 2.6355 | 0.6486 | | 0.0 | 102.0068 | 7313 | 2.8393 | 0.6622 | | 0.0001 | 103.0068 | 7384 | 2.1938 | 0.6892 | | 0.0001 | 104.0068 | 7455 | 2.2225 | 0.7027 | | 0.1038 | 105.0068 | 7526 | 2.4167 | 0.7027 | | 0.0001 | 106.0068 | 7597 | 2.2465 | 0.7162 | | 0.0001 | 107.0068 | 7668 | 2.4677 | 0.7027 | | 0.0333 | 108.0068 | 7739 | 2.4546 | 0.6622 | | 0.0119 | 109.0068 | 7810 | 2.5811 | 0.6892 | | 0.0001 | 110.0068 | 7881 | 2.2874 | 0.7162 | | 0.0 | 111.0068 | 7952 | 2.1970 | 0.7297 | | 0.0 | 112.0068 | 8023 | 2.2009 | 0.7432 | | 0.0001 | 113.0068 | 8094 | 2.2554 | 0.7432 | | 0.0 | 114.0068 | 8165 | 2.2652 | 0.7162 | | 0.0 | 115.0068 | 8236 | 2.3248 | 0.7162 | | 0.0001 | 116.0068 | 8307 | 2.5589 | 0.6892 | | 0.0 | 117.0068 | 8378 | 2.2266 | 0.7568 | | 0.0 | 118.0068 | 8449 | 2.2807 | 0.6892 | | 0.0 | 119.0068 | 8520 | 2.2664 | 0.7432 | | 0.0 | 120.0068 | 8591 | 2.1452 | 0.7162 | | 0.0001 | 121.0068 | 8662 | 2.2492 | 0.7297 | | 0.0 | 122.0068 | 8733 | 2.2303 | 0.7432 | | 0.0 | 123.0068 | 8804 | 2.2320 | 0.7432 | | 0.0 | 124.0068 | 8875 | 2.2220 | 0.7162 | | 0.0 | 125.0068 | 8946 | 2.2343 | 0.7027 | | 0.0 | 126.0068 | 9017 | 2.3466 | 0.7162 | | 0.0 | 127.0068 | 9088 | 2.4283 | 0.7027 | | 0.0 | 128.0068 | 9159 | 2.3447 | 0.7162 | | 0.0 | 129.0068 | 9230 | 2.7482 | 0.6892 | | 0.0 | 130.0068 | 9301 | 2.4948 | 0.7297 | | 0.0 | 131.0068 | 9372 | 2.5561 | 0.7027 | | 0.0 | 132.0068 | 9443 | 2.4132 | 0.7162 | | 0.0 | 133.0068 | 9514 | 2.3921 | 0.7297 | | 0.0 | 134.0068 | 9585 | 2.3964 | 0.7297 | | 0.0 | 135.0068 | 9656 | 2.5452 | 0.7027 | | 0.0 | 136.0068 | 9727 | 2.5288 | 0.7027 | | 0.0 | 137.0068 | 9798 | 2.4979 | 0.7162 | | 0.0 | 138.0068 | 9869 | 2.4991 | 0.7162 | | 0.0001 | 139.0068 | 9940 | 2.4993 | 0.7162 | | 0.0 | 140.0068 | 10011 | 2.5002 | 0.7027 | | 0.0 | 141.0068 | 10082 | 2.5028 | 0.7027 | | 0.0 | 142.0068 | 10153 | 2.5063 | 0.7027 | | 0.0 | 143.0068 | 10224 | 2.5081 | 0.7027 | | 0.0 | 144.0068 | 10295 | 2.5087 | 0.7027 | | 0.0 | 145.0068 | 10366 | 2.5091 | 0.7027 | | 0.0 | 146.0068 | 10437 | 2.5093 | 0.7027 | | 0.0 | 147.006 | 10500 | 2.5051 | 0.7027 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.0.1+cu117 - Datasets 3.0.1 - Tokenizers 0.20.0