--- 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-CPC-Dicotomized-v7-tricot results: [] --- # MAE-CT-CPC-Dicotomized-v7-tricot 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: 5.4223 - Accuracy: 0.3077 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 7900 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 1.0989 | 0.0101 | 80 | 1.1031 | 0.3191 | | 1.0889 | 1.0101 | 160 | 1.1058 | 0.3404 | | 1.0739 | 2.0101 | 240 | 1.1233 | 0.4043 | | 1.0036 | 3.0101 | 320 | 1.1596 | 0.2766 | | 1.0706 | 4.0101 | 400 | 1.1731 | 0.2553 | | 0.9669 | 5.0101 | 480 | 1.1228 | 0.3191 | | 1.0233 | 6.0101 | 560 | 1.1490 | 0.4043 | | 0.8492 | 7.0101 | 640 | 1.2636 | 0.3830 | | 0.8842 | 8.0101 | 720 | 1.4061 | 0.3617 | | 0.6599 | 9.0101 | 800 | 1.3445 | 0.2979 | | 0.6723 | 10.0101 | 880 | 1.4072 | 0.3617 | | 0.604 | 11.0101 | 960 | 1.4199 | 0.3617 | | 0.4959 | 12.0101 | 1040 | 1.5689 | 0.3617 | | 0.3758 | 13.0101 | 1120 | 1.7867 | 0.3617 | | 0.6257 | 14.0101 | 1200 | 1.9218 | 0.3617 | | 0.3693 | 15.0101 | 1280 | 2.0988 | 0.3191 | | 0.5933 | 16.0101 | 1360 | 1.8413 | 0.4043 | | 0.202 | 17.0101 | 1440 | 2.7537 | 0.3191 | | 0.1454 | 18.0101 | 1520 | 2.4612 | 0.4255 | | 0.1332 | 19.0101 | 1600 | 3.0944 | 0.3404 | | 0.9193 | 20.0101 | 1680 | 2.8691 | 0.4043 | | 0.1201 | 21.0101 | 1760 | 3.0564 | 0.4255 | | 0.1716 | 22.0101 | 1840 | 3.3907 | 0.3404 | | 0.0402 | 23.0101 | 1920 | 3.7917 | 0.3191 | | 0.0709 | 24.0101 | 2000 | 3.5487 | 0.4043 | | 0.1021 | 25.0101 | 2080 | 3.9004 | 0.4043 | | 0.0029 | 26.0101 | 2160 | 4.1949 | 0.3617 | | 0.1352 | 27.0101 | 2240 | 4.5038 | 0.3617 | | 0.0173 | 28.0101 | 2320 | 3.9352 | 0.3830 | | 0.0012 | 29.0101 | 2400 | 4.3234 | 0.4043 | | 0.0007 | 30.0101 | 2480 | 4.2877 | 0.3830 | | 0.2292 | 31.0101 | 2560 | 4.7297 | 0.3191 | | 0.0004 | 32.0101 | 2640 | 4.4710 | 0.3830 | | 0.0361 | 33.0101 | 2720 | 4.2391 | 0.4255 | | 0.0002 | 34.0101 | 2800 | 4.2256 | 0.4043 | | 0.0082 | 35.0101 | 2880 | 5.0734 | 0.3404 | | 0.0318 | 36.0101 | 2960 | 4.0735 | 0.4255 | | 0.0002 | 37.0101 | 3040 | 5.1464 | 0.2553 | | 0.0003 | 38.0101 | 3120 | 4.6340 | 0.4043 | | 0.48 | 39.0101 | 3200 | 4.3370 | 0.4255 | | 0.0002 | 40.0101 | 3280 | 4.5820 | 0.3617 | | 0.0002 | 41.0101 | 3360 | 5.0157 | 0.3191 | | 0.1209 | 42.0101 | 3440 | 4.3109 | 0.3830 | | 0.0001 | 43.0101 | 3520 | 4.4596 | 0.4043 | | 0.0109 | 44.0101 | 3600 | 4.4251 | 0.3830 | | 0.0001 | 45.0101 | 3680 | 5.2962 | 0.2979 | | 0.1516 | 46.0101 | 3760 | 4.2314 | 0.4043 | | 0.0001 | 47.0101 | 3840 | 4.0705 | 0.5319 | | 0.0001 | 48.0101 | 3920 | 4.5586 | 0.4255 | | 0.0266 | 49.0101 | 4000 | 4.9479 | 0.4043 | | 0.0001 | 50.0101 | 4080 | 4.3270 | 0.4468 | | 0.1307 | 51.0101 | 4160 | 4.7948 | 0.3830 | | 0.0019 | 52.0101 | 4240 | 4.3638 | 0.3617 | | 0.0001 | 53.0101 | 4320 | 4.5863 | 0.4255 | | 0.0001 | 54.0101 | 4400 | 4.7373 | 0.4255 | | 0.0006 | 55.0101 | 4480 | 3.9066 | 0.4468 | | 0.0001 | 56.0101 | 4560 | 4.0314 | 0.4681 | | 0.0001 | 57.0101 | 4640 | 4.0581 | 0.5106 | | 0.0001 | 58.0101 | 4720 | 5.0045 | 0.3830 | | 0.0001 | 59.0101 | 4800 | 4.0895 | 0.4255 | | 0.0713 | 60.0101 | 4880 | 5.0429 | 0.4255 | | 0.0017 | 61.0101 | 4960 | 4.7870 | 0.4255 | | 0.0676 | 62.0101 | 5040 | 5.0957 | 0.3830 | | 0.0 | 63.0101 | 5120 | 4.6062 | 0.4043 | | 0.0045 | 64.0101 | 5200 | 5.2459 | 0.3830 | | 0.0943 | 65.0101 | 5280 | 5.0856 | 0.3617 | | 0.0002 | 66.0101 | 5360 | 4.4492 | 0.4894 | | 0.0002 | 67.0101 | 5440 | 5.1795 | 0.4043 | | 0.0007 | 68.0101 | 5520 | 4.3202 | 0.4681 | | 0.1678 | 69.0101 | 5600 | 4.8688 | 0.4043 | | 0.0001 | 70.0101 | 5680 | 5.2880 | 0.4043 | | 0.0 | 71.0101 | 5760 | 5.1151 | 0.4255 | | 0.0005 | 72.0101 | 5840 | 4.5667 | 0.4255 | | 0.0 | 73.0101 | 5920 | 4.2883 | 0.4681 | | 0.0 | 74.0101 | 6000 | 4.6848 | 0.4255 | | 0.0 | 75.0101 | 6080 | 4.8157 | 0.4468 | | 0.0 | 76.0101 | 6160 | 4.8248 | 0.4468 | | 0.0 | 77.0101 | 6240 | 4.5636 | 0.4894 | | 0.0 | 78.0101 | 6320 | 4.5817 | 0.4255 | | 0.0001 | 79.0101 | 6400 | 4.7743 | 0.3830 | | 0.0001 | 80.0101 | 6480 | 4.9000 | 0.4043 | | 0.0002 | 81.0101 | 6560 | 4.7669 | 0.4255 | | 0.0 | 82.0101 | 6640 | 4.8225 | 0.4468 | | 0.0 | 83.0101 | 6720 | 4.8331 | 0.4468 | | 0.0 | 84.0101 | 6800 | 4.7154 | 0.4468 | | 0.0 | 85.0101 | 6880 | 4.7169 | 0.4468 | | 0.0 | 86.0101 | 6960 | 4.9004 | 0.4255 | | 0.0 | 87.0101 | 7040 | 4.9092 | 0.4255 | | 0.0 | 88.0101 | 7120 | 4.8941 | 0.4255 | | 0.0 | 89.0101 | 7200 | 4.7898 | 0.4255 | | 0.0 | 90.0101 | 7280 | 4.8271 | 0.4468 | | 0.0 | 91.0101 | 7360 | 4.8320 | 0.4468 | | 0.0 | 92.0101 | 7440 | 4.8274 | 0.4468 | | 0.0 | 93.0101 | 7520 | 4.8269 | 0.4468 | | 0.0 | 94.0101 | 7600 | 4.8785 | 0.3830 | | 0.0 | 95.0101 | 7680 | 4.9640 | 0.4255 | | 0.0 | 96.0101 | 7760 | 4.9480 | 0.4255 | | 0.0 | 97.0101 | 7840 | 4.9404 | 0.4255 | | 0.0 | 98.0076 | 7900 | 4.9351 | 0.4255 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.0.1+cu117 - Datasets 3.0.1 - Tokenizers 0.20.0