--- 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_split2 results: [] --- # MAE-CT-M1N0-M12_v8_split2 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: 0.5597 - Accuracy: 0.7260 ## 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: 6200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.6375 | 0.0102 | 63 | 0.6993 | 0.5161 | | 0.7565 | 1.0102 | 126 | 0.7252 | 0.5161 | | 0.6926 | 2.0102 | 189 | 0.7296 | 0.5161 | | 0.5636 | 3.0102 | 252 | 0.7618 | 0.5161 | | 0.4721 | 4.0102 | 315 | 1.2407 | 0.5161 | | 0.7569 | 5.0102 | 378 | 0.8010 | 0.5161 | | 0.384 | 6.0102 | 441 | 0.6036 | 0.6774 | | 0.6542 | 7.0102 | 504 | 0.5455 | 0.8065 | | 0.3615 | 8.0102 | 567 | 1.3506 | 0.5484 | | 0.2246 | 9.0102 | 630 | 1.5499 | 0.5806 | | 0.7929 | 10.0102 | 693 | 1.0719 | 0.6452 | | 0.5963 | 11.0102 | 756 | 0.9215 | 0.6129 | | 0.1342 | 12.0102 | 819 | 0.9188 | 0.6452 | | 0.2511 | 13.0102 | 882 | 1.4410 | 0.6452 | | 0.5877 | 14.0102 | 945 | 2.3550 | 0.5161 | | 0.3261 | 15.0102 | 1008 | 1.0729 | 0.6774 | | 0.0425 | 16.0102 | 1071 | 2.5330 | 0.5806 | | 0.174 | 17.0102 | 1134 | 2.8190 | 0.5806 | | 0.1972 | 18.0102 | 1197 | 2.4491 | 0.5484 | | 0.2264 | 19.0102 | 1260 | 1.9345 | 0.6774 | | 0.0862 | 20.0102 | 1323 | 3.3695 | 0.5161 | | 0.0998 | 21.0102 | 1386 | 1.4091 | 0.7742 | | 0.311 | 22.0102 | 1449 | 2.7629 | 0.5484 | | 0.0481 | 23.0102 | 1512 | 2.0506 | 0.6452 | | 0.2109 | 24.0102 | 1575 | 2.5990 | 0.5806 | | 0.179 | 25.0102 | 1638 | 2.7815 | 0.5806 | | 0.0002 | 26.0102 | 1701 | 3.6719 | 0.5161 | | 0.0996 | 27.0102 | 1764 | 3.7618 | 0.5161 | | 0.0002 | 28.0102 | 1827 | 3.3375 | 0.5484 | | 0.0004 | 29.0102 | 1890 | 2.8750 | 0.6129 | | 0.0001 | 30.0102 | 1953 | 2.5867 | 0.6774 | | 0.1188 | 31.0102 | 2016 | 1.8263 | 0.6774 | | 0.0295 | 32.0102 | 2079 | 3.2699 | 0.5806 | | 0.1931 | 33.0102 | 2142 | 3.3532 | 0.5806 | | 0.0002 | 34.0102 | 2205 | 4.2001 | 0.5161 | | 0.0001 | 35.0102 | 2268 | 3.3819 | 0.5484 | | 0.0001 | 36.0102 | 2331 | 2.2776 | 0.7097 | | 0.0007 | 37.0102 | 2394 | 2.8516 | 0.5806 | | 0.0001 | 38.0102 | 2457 | 4.0420 | 0.5161 | | 0.0002 | 39.0102 | 2520 | 2.5901 | 0.6452 | | 0.0001 | 40.0102 | 2583 | 3.5043 | 0.5806 | | 0.0001 | 41.0102 | 2646 | 3.5424 | 0.5806 | | 0.0001 | 42.0102 | 2709 | 3.8740 | 0.5484 | | 0.0001 | 43.0102 | 2772 | 3.5726 | 0.5806 | | 0.0004 | 44.0102 | 2835 | 3.2184 | 0.5806 | | 0.0 | 45.0102 | 2898 | 3.3347 | 0.5806 | | 0.0001 | 46.0102 | 2961 | 3.8206 | 0.5806 | | 0.0 | 47.0102 | 3024 | 3.7951 | 0.5484 | | 0.0 | 48.0102 | 3087 | 2.7604 | 0.6774 | | 0.0 | 49.0102 | 3150 | 4.3949 | 0.5484 | | 0.0 | 50.0102 | 3213 | 2.8947 | 0.6774 | | 0.0 | 51.0102 | 3276 | 4.2413 | 0.5161 | | 0.1268 | 52.0102 | 3339 | 2.3339 | 0.7097 | | 0.0 | 53.0102 | 3402 | 3.4769 | 0.6129 | | 0.0 | 54.0102 | 3465 | 3.5142 | 0.5806 | | 0.0 | 55.0102 | 3528 | 3.5718 | 0.5161 | | 0.0036 | 56.0102 | 3591 | 4.1867 | 0.4839 | | 0.0026 | 57.0102 | 3654 | 2.7411 | 0.6452 | | 0.0 | 58.0102 | 3717 | 4.0464 | 0.5484 | | 0.0001 | 59.0102 | 3780 | 3.6255 | 0.5806 | | 0.0 | 60.0102 | 3843 | 4.7292 | 0.5161 | | 0.1406 | 61.0102 | 3906 | 3.9876 | 0.5806 | | 0.0 | 62.0102 | 3969 | 3.4099 | 0.6129 | | 0.0 | 63.0102 | 4032 | 3.2674 | 0.5806 | | 0.0 | 64.0102 | 4095 | 3.9749 | 0.5806 | | 0.0 | 65.0102 | 4158 | 3.3262 | 0.6129 | | 0.0 | 66.0102 | 4221 | 2.5556 | 0.7097 | | 0.2639 | 67.0102 | 4284 | 3.6954 | 0.6129 | | 0.0011 | 68.0102 | 4347 | 3.2776 | 0.5806 | | 0.0 | 69.0102 | 4410 | 3.6620 | 0.5806 | | 0.0 | 70.0102 | 4473 | 3.5887 | 0.5806 | | 0.0 | 71.0102 | 4536 | 4.5040 | 0.5484 | | 0.0 | 72.0102 | 4599 | 3.8666 | 0.5484 | | 0.0 | 73.0102 | 4662 | 4.0017 | 0.5484 | | 0.0 | 74.0102 | 4725 | 3.9422 | 0.5484 | | 0.0001 | 75.0102 | 4788 | 4.5397 | 0.5484 | | 0.0 | 76.0102 | 4851 | 3.8405 | 0.5806 | | 0.0 | 77.0102 | 4914 | 3.9992 | 0.5806 | | 0.0 | 78.0102 | 4977 | 3.9722 | 0.5806 | | 0.0 | 79.0102 | 5040 | 3.9421 | 0.5806 | | 0.2333 | 80.0102 | 5103 | 4.0817 | 0.5484 | | 0.0 | 81.0102 | 5166 | 3.6669 | 0.6129 | | 0.0 | 82.0102 | 5229 | 3.6607 | 0.6129 | | 0.0 | 83.0102 | 5292 | 3.6873 | 0.6129 | | 0.0 | 84.0102 | 5355 | 4.5979 | 0.5484 | | 0.0 | 85.0102 | 5418 | 3.8881 | 0.5806 | | 0.0 | 86.0102 | 5481 | 4.6014 | 0.5484 | | 0.0 | 87.0102 | 5544 | 3.7988 | 0.6129 | | 0.0 | 88.0102 | 5607 | 3.8047 | 0.6129 | | 0.0 | 89.0102 | 5670 | 3.8106 | 0.6129 | | 0.0001 | 90.0102 | 5733 | 4.1468 | 0.5806 | | 0.0 | 91.0102 | 5796 | 4.3700 | 0.5484 | | 0.0 | 92.0102 | 5859 | 4.2392 | 0.5484 | | 0.0 | 93.0102 | 5922 | 4.2085 | 0.5484 | | 0.0 | 94.0102 | 5985 | 4.2017 | 0.5806 | | 0.0 | 95.0102 | 6048 | 4.1972 | 0.5806 | | 0.0 | 96.0102 | 6111 | 4.1925 | 0.5806 | | 0.0 | 97.0102 | 6174 | 4.1911 | 0.5806 | | 0.0 | 98.0042 | 6200 | 4.1910 | 0.5806 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.0.1+cu117 - Datasets 3.0.1 - Tokenizers 0.20.0