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--- |
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license: cc-by-nc-4.0 |
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base_model: MCG-NJU/videomae-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: videomae-base-finetuned-subset-100epochs |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# videomae-base-finetuned-subset-100epochs |
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This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co./MCG-NJU/videomae-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7077 |
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- Accuracy: 0.7685 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 5550 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 1.6657 | 0.01 | 56 | 1.6248 | 0.2258 | |
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| 1.6109 | 1.01 | 112 | 1.5601 | 0.3917 | |
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| 1.5669 | 2.01 | 168 | 1.5563 | 0.3733 | |
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| 1.45 | 3.01 | 224 | 1.0988 | 0.5991 | |
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| 1.1208 | 4.01 | 280 | 1.2279 | 0.5714 | |
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| 1.1588 | 5.01 | 336 | 0.8424 | 0.7097 | |
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| 1.0834 | 6.01 | 392 | 1.1035 | 0.5346 | |
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| 1.2194 | 7.01 | 448 | 1.0749 | 0.4839 | |
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| 0.8462 | 8.01 | 504 | 0.8755 | 0.6406 | |
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| 1.058 | 9.01 | 560 | 0.9025 | 0.6498 | |
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| 1.0163 | 10.01 | 616 | 1.2588 | 0.4839 | |
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| 1.0639 | 11.01 | 672 | 0.8928 | 0.6359 | |
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| 0.9317 | 12.01 | 728 | 0.8825 | 0.6221 | |
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| 0.9038 | 13.01 | 784 | 0.8765 | 0.5622 | |
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| 0.9155 | 14.01 | 840 | 0.8431 | 0.7005 | |
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| 1.0731 | 15.01 | 896 | 0.8175 | 0.7005 | |
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| 0.6864 | 16.01 | 952 | 1.0591 | 0.5853 | |
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| 0.9537 | 17.01 | 1008 | 0.9703 | 0.6221 | |
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| 0.7499 | 18.01 | 1064 | 0.8371 | 0.5806 | |
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| 0.7142 | 19.01 | 1120 | 0.9132 | 0.6636 | |
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| 0.675 | 20.01 | 1176 | 0.7597 | 0.6728 | |
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| 0.604 | 21.01 | 1232 | 1.2004 | 0.5714 | |
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| 0.7738 | 22.01 | 1288 | 1.0633 | 0.5668 | |
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| 0.7651 | 23.01 | 1344 | 0.6865 | 0.6820 | |
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| 0.6292 | 24.01 | 1400 | 0.7607 | 0.6912 | |
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| 0.7387 | 25.01 | 1456 | 1.3038 | 0.5346 | |
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| 0.7038 | 26.01 | 1512 | 1.2832 | 0.5530 | |
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| 0.7565 | 27.01 | 1568 | 0.8128 | 0.7005 | |
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| 0.6516 | 28.01 | 1624 | 1.0893 | 0.5392 | |
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| 0.7074 | 29.01 | 1680 | 1.0894 | 0.5991 | |
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| 0.4902 | 30.01 | 1736 | 1.0695 | 0.5622 | |
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| 0.4563 | 31.01 | 1792 | 1.2922 | 0.5300 | |
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| 0.7543 | 32.01 | 1848 | 0.8960 | 0.6820 | |
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| 0.7467 | 33.01 | 1904 | 0.7861 | 0.7465 | |
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| 0.6459 | 34.01 | 1960 | 1.2835 | 0.5622 | |
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| 0.7296 | 35.01 | 2016 | 1.0303 | 0.5806 | |
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| 0.5 | 36.01 | 2072 | 0.8924 | 0.6129 | |
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| 0.5181 | 37.01 | 2128 | 0.8769 | 0.7235 | |
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| 0.5225 | 38.01 | 2184 | 0.7288 | 0.7512 | |
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| 0.5617 | 39.01 | 2240 | 0.6330 | 0.7926 | |
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| 0.677 | 40.01 | 2296 | 0.7733 | 0.7419 | |
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| 0.6891 | 41.01 | 2352 | 0.7463 | 0.8157 | |
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| 0.6662 | 42.01 | 2408 | 0.9304 | 0.7235 | |
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| 0.4602 | 43.01 | 2464 | 1.5115 | 0.5207 | |
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| 0.581 | 44.01 | 2520 | 1.2296 | 0.6175 | |
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| 0.5418 | 45.01 | 2576 | 1.0070 | 0.6221 | |
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| 0.5199 | 46.01 | 2632 | 1.1344 | 0.6083 | |
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| 0.6876 | 47.01 | 2688 | 0.9800 | 0.5760 | |
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| 0.5165 | 48.01 | 2744 | 1.3709 | 0.5069 | |
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| 0.5727 | 49.01 | 2800 | 0.9960 | 0.6866 | |
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| 0.3698 | 50.01 | 2856 | 1.2246 | 0.5484 | |
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| 0.5836 | 51.01 | 2912 | 0.9892 | 0.6866 | |
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| 0.6017 | 52.01 | 2968 | 0.9388 | 0.6590 | |
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| 0.4851 | 53.01 | 3024 | 1.1415 | 0.6590 | |
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| 0.3038 | 54.01 | 3080 | 0.9413 | 0.6959 | |
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| 0.6075 | 55.01 | 3136 | 1.0467 | 0.6129 | |
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| 0.4474 | 56.01 | 3192 | 0.8436 | 0.6866 | |
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| 0.3711 | 57.01 | 3248 | 0.8994 | 0.6774 | |
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| 0.5279 | 58.01 | 3304 | 0.8859 | 0.7189 | |
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| 0.6032 | 59.01 | 3360 | 1.2931 | 0.6498 | |
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| 0.3282 | 60.01 | 3416 | 0.9435 | 0.7143 | |
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| 0.3506 | 61.01 | 3472 | 1.0971 | 0.6728 | |
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| 0.3169 | 62.01 | 3528 | 0.9101 | 0.7512 | |
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| 0.438 | 63.01 | 3584 | 1.4072 | 0.6359 | |
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| 0.5208 | 64.01 | 3640 | 1.2648 | 0.6544 | |
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| 0.4563 | 65.01 | 3696 | 1.1162 | 0.6498 | |
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| 0.6693 | 66.01 | 3752 | 1.8558 | 0.5576 | |
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| 0.5599 | 67.01 | 3808 | 1.6574 | 0.5392 | |
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| 0.4751 | 68.01 | 3864 | 1.1883 | 0.6129 | |
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| 0.6489 | 69.01 | 3920 | 1.2733 | 0.6129 | |
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| 0.4229 | 70.01 | 3976 | 1.0994 | 0.6682 | |
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| 0.4194 | 71.01 | 4032 | 1.1464 | 0.6175 | |
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| 0.2121 | 72.01 | 4088 | 1.1798 | 0.6175 | |
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| 0.4106 | 73.01 | 4144 | 1.3294 | 0.5806 | |
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| 0.3962 | 74.01 | 4200 | 1.4209 | 0.6359 | |
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| 0.2963 | 75.01 | 4256 | 1.5016 | 0.5945 | |
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| 0.5436 | 76.01 | 4312 | 1.5647 | 0.5484 | |
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| 0.4115 | 77.01 | 4368 | 1.4309 | 0.6037 | |
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| 0.1635 | 78.01 | 4424 | 1.3660 | 0.6452 | |
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| 0.2931 | 79.01 | 4480 | 1.3299 | 0.6498 | |
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| 0.5154 | 80.01 | 4536 | 1.6550 | 0.5806 | |
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| 0.2993 | 81.01 | 4592 | 1.6520 | 0.5991 | |
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| 0.4391 | 82.01 | 4648 | 1.3823 | 0.6406 | |
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| 0.485 | 83.01 | 4704 | 1.4860 | 0.6037 | |
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| 0.3313 | 84.01 | 4760 | 1.3875 | 0.6175 | |
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| 0.4194 | 85.01 | 4816 | 1.4334 | 0.5899 | |
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| 0.4515 | 86.01 | 4872 | 1.6489 | 0.5991 | |
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| 0.3283 | 87.01 | 4928 | 1.4549 | 0.6083 | |
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| 0.1914 | 88.01 | 4984 | 1.3415 | 0.6267 | |
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| 0.2142 | 89.01 | 5040 | 1.6426 | 0.6267 | |
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| 0.3121 | 90.01 | 5096 | 1.6999 | 0.6037 | |
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| 0.367 | 91.01 | 5152 | 1.4683 | 0.6083 | |
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| 0.178 | 92.01 | 5208 | 1.4665 | 0.6267 | |
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| 0.3972 | 93.01 | 5264 | 1.3464 | 0.6452 | |
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| 0.224 | 94.01 | 5320 | 1.5009 | 0.6175 | |
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| 0.1848 | 95.01 | 5376 | 1.5068 | 0.6129 | |
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| 0.2776 | 96.01 | 5432 | 1.5383 | 0.6175 | |
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| 0.3506 | 97.01 | 5488 | 1.5356 | 0.6129 | |
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| 0.401 | 98.01 | 5544 | 1.5504 | 0.6175 | |
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| 0.3466 | 99.0 | 5550 | 1.5505 | 0.6175 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 1.13.1 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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