--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-subset results: [] --- # videomae-base-finetuned-subset 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: 0.7700 - Accuracy: 0.6713 ## 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: 5e-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: 11100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 1.638 | 0.01 | 112 | 1.5736 | 0.1567 | | 1.5845 | 1.01 | 224 | 1.5841 | 0.2719 | | 1.4522 | 2.01 | 336 | 1.6293 | 0.2350 | | 1.3111 | 3.01 | 448 | 1.0450 | 0.6037 | | 1.2849 | 4.01 | 560 | 1.3186 | 0.4608 | | 1.3246 | 5.01 | 672 | 1.1759 | 0.5161 | | 1.3801 | 6.01 | 784 | 1.2188 | 0.4608 | | 1.3228 | 7.01 | 896 | 0.9895 | 0.6406 | | 0.9706 | 8.01 | 1008 | 1.1265 | 0.6129 | | 1.2483 | 9.01 | 1120 | 1.2352 | 0.5484 | | 0.9394 | 10.01 | 1232 | 1.2345 | 0.4977 | | 0.8285 | 11.01 | 1344 | 0.8702 | 0.6682 | | 1.1175 | 12.01 | 1456 | 0.9073 | 0.6406 | | 1.093 | 13.01 | 1568 | 0.9210 | 0.5576 | | 0.8364 | 14.01 | 1680 | 0.9316 | 0.6590 | | 0.766 | 15.01 | 1792 | 0.7628 | 0.7742 | | 0.7702 | 16.01 | 1904 | 0.8982 | 0.6682 | | 0.9184 | 17.01 | 2016 | 1.1010 | 0.6221 | | 0.7309 | 18.01 | 2128 | 0.8245 | 0.6866 | | 0.9575 | 19.01 | 2240 | 0.9029 | 0.7097 | | 0.8233 | 20.01 | 2352 | 1.2445 | 0.5161 | | 0.7643 | 21.01 | 2464 | 0.9558 | 0.6498 | | 0.6722 | 22.01 | 2576 | 1.1864 | 0.5714 | | 0.8441 | 23.01 | 2688 | 0.9690 | 0.7235 | | 0.7971 | 24.01 | 2800 | 0.9349 | 0.6774 | | 0.8296 | 25.01 | 2912 | 1.4574 | 0.4516 | | 0.8613 | 26.01 | 3024 | 0.8688 | 0.7189 | | 0.5614 | 27.01 | 3136 | 1.2101 | 0.6083 | | 0.6971 | 28.01 | 3248 | 1.3006 | 0.4654 | | 0.9642 | 29.01 | 3360 | 0.9573 | 0.6313 | | 0.836 | 30.01 | 3472 | 1.1268 | 0.6221 | | 0.7166 | 31.01 | 3584 | 1.2384 | 0.5622 | | 0.9302 | 32.01 | 3696 | 1.0620 | 0.5991 | | 0.7729 | 33.01 | 3808 | 1.3253 | 0.5622 | | 0.8005 | 34.01 | 3920 | 1.4979 | 0.4931 | | 0.8025 | 35.01 | 4032 | 0.9786 | 0.5668 | | 0.881 | 36.01 | 4144 | 0.8477 | 0.6544 | | 0.5343 | 37.01 | 4256 | 1.3107 | 0.6544 | | 0.5611 | 38.01 | 4368 | 0.9520 | 0.6866 | | 0.6824 | 39.01 | 4480 | 0.7909 | 0.7281 | | 0.6146 | 40.01 | 4592 | 1.0886 | 0.6175 | | 1.0098 | 41.01 | 4704 | 1.0434 | 0.6313 | | 0.5555 | 42.01 | 4816 | 0.9603 | 0.6912 | | 0.4578 | 43.01 | 4928 | 1.2341 | 0.5945 | | 0.5883 | 44.01 | 5040 | 1.2559 | 0.6359 | | 0.3579 | 45.01 | 5152 | 1.2459 | 0.5622 | | 0.7936 | 46.01 | 5264 | 1.2685 | 0.6083 | | 0.4331 | 47.01 | 5376 | 0.9118 | 0.7097 | | 0.8989 | 48.01 | 5488 | 1.3406 | 0.5806 | | 0.7674 | 49.01 | 5600 | 1.5231 | 0.5484 | | 0.8136 | 50.01 | 5712 | 1.2210 | 0.6221 | | 0.6583 | 51.01 | 5824 | 0.9262 | 0.7051 | | 0.4305 | 52.01 | 5936 | 1.0339 | 0.6959 | | 0.7197 | 53.01 | 6048 | 1.1948 | 0.6682 | | 0.7143 | 54.01 | 6160 | 1.1851 | 0.6774 | | 0.5441 | 55.01 | 6272 | 1.0351 | 0.6636 | | 0.6443 | 56.01 | 6384 | 1.0297 | 0.6866 | | 0.7747 | 57.01 | 6496 | 1.5174 | 0.5991 | | 0.5943 | 58.01 | 6608 | 1.1961 | 0.6452 | | 0.5781 | 59.01 | 6720 | 1.2187 | 0.7143 | | 0.6913 | 60.01 | 6832 | 1.1590 | 0.6728 | | 0.6186 | 61.01 | 6944 | 1.0495 | 0.7235 | | 0.5185 | 62.01 | 7056 | 0.9844 | 0.7051 | | 0.4077 | 63.01 | 7168 | 1.3194 | 0.6313 | | 0.8217 | 64.01 | 7280 | 1.2620 | 0.6636 | | 0.5273 | 65.01 | 7392 | 1.0395 | 0.7373 | | 0.9002 | 66.01 | 7504 | 1.5225 | 0.5806 | | 0.5763 | 67.01 | 7616 | 1.2559 | 0.6406 | | 1.0535 | 68.01 | 7728 | 1.2646 | 0.6498 | | 1.0064 | 69.01 | 7840 | 1.1533 | 0.6866 | | 0.332 | 70.01 | 7952 | 1.0438 | 0.7005 | | 0.3978 | 71.01 | 8064 | 1.0248 | 0.7051 | | 0.4459 | 72.01 | 8176 | 1.0926 | 0.7465 | | 0.511 | 73.01 | 8288 | 1.1233 | 0.7143 | | 0.7933 | 74.01 | 8400 | 1.1535 | 0.7189 | | 0.3739 | 75.01 | 8512 | 1.3056 | 0.6912 | | 0.6976 | 76.01 | 8624 | 1.3159 | 0.6682 | | 0.5453 | 77.01 | 8736 | 1.4541 | 0.6359 | | 0.2915 | 78.01 | 8848 | 1.2601 | 0.7051 | | 0.6552 | 79.01 | 8960 | 1.5338 | 0.6544 | | 0.5067 | 80.01 | 9072 | 1.6630 | 0.6037 | | 0.5134 | 81.01 | 9184 | 1.4740 | 0.6406 | | 0.7271 | 82.01 | 9296 | 1.2171 | 0.7097 | | 0.719 | 83.01 | 9408 | 1.3653 | 0.6406 | | 0.1955 | 84.01 | 9520 | 1.4696 | 0.6544 | | 0.5761 | 85.01 | 9632 | 1.3334 | 0.6636 | | 0.7094 | 86.01 | 9744 | 1.2673 | 0.6912 | | 0.5186 | 87.01 | 9856 | 1.3147 | 0.6866 | | 0.6876 | 88.01 | 9968 | 1.2622 | 0.7051 | | 0.4912 | 89.01 | 10080 | 1.3054 | 0.7189 | | 0.194 | 90.01 | 10192 | 1.3244 | 0.6959 | | 0.6916 | 91.01 | 10304 | 1.1800 | 0.7327 | | 0.5735 | 92.01 | 10416 | 1.1056 | 0.7419 | | 0.2122 | 93.01 | 10528 | 1.1070 | 0.7281 | | 0.1434 | 94.01 | 10640 | 1.1776 | 0.7097 | | 0.4681 | 95.01 | 10752 | 1.1505 | 0.7327 | | 0.2856 | 96.01 | 10864 | 1.1203 | 0.7235 | | 0.6509 | 97.01 | 10976 | 1.1502 | 0.7189 | | 0.1881 | 98.01 | 11088 | 1.1474 | 0.7189 | | 0.5577 | 99.0 | 11100 | 1.1473 | 0.7189 | ### Framework versions - Transformers 4.36.2 - Pytorch 1.13.1 - Datasets 2.16.1 - Tokenizers 0.15.0