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--- |
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license: cc-by-nc-4.0 |
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tags: |
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- generated_from_trainer |
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base_model: MCG-NJU/videomae-large-finetuned-kinetics |
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metrics: |
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- accuracy |
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model-index: |
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- name: MAE-CT-CPC-Dicotomized-v3 |
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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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# MAE-CT-CPC-Dicotomized-v3 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8472 |
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- Accuracy: 0.7317 |
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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: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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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: 4000 |
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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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| 0.5652 | 0.02 | 81 | 0.6260 | 0.6829 | |
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| 0.6109 | 1.02 | 162 | 0.6052 | 0.6829 | |
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| 0.7142 | 2.02 | 243 | 0.6207 | 0.6829 | |
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| 0.5037 | 3.02 | 324 | 0.6219 | 0.6829 | |
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| 0.5177 | 4.02 | 405 | 0.6456 | 0.6829 | |
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| 0.5363 | 5.02 | 486 | 0.5791 | 0.6829 | |
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| 0.6797 | 6.02 | 567 | 0.6295 | 0.6829 | |
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| 0.557 | 7.02 | 648 | 0.5182 | 0.7073 | |
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| 0.5645 | 8.02 | 729 | 0.4608 | 0.7073 | |
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| 0.4484 | 9.02 | 810 | 0.4721 | 0.7805 | |
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| 0.3354 | 10.02 | 891 | 0.7011 | 0.7317 | |
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| 0.2875 | 11.02 | 972 | 0.4498 | 0.7805 | |
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| 0.3535 | 12.02 | 1053 | 0.8161 | 0.7561 | |
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| 0.294 | 13.02 | 1134 | 0.5175 | 0.7805 | |
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| 0.1907 | 14.02 | 1215 | 0.3750 | 0.8537 | |
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| 0.2533 | 15.02 | 1296 | 0.6400 | 0.7805 | |
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| 0.2499 | 16.02 | 1377 | 0.9233 | 0.7805 | |
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| 0.2726 | 17.02 | 1458 | 1.0686 | 0.7805 | |
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| 0.4447 | 18.02 | 1539 | 1.0433 | 0.7073 | |
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| 0.1554 | 19.02 | 1620 | 1.0791 | 0.8293 | |
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| 0.0163 | 20.02 | 1701 | 1.0939 | 0.7317 | |
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| 0.0507 | 21.02 | 1782 | 1.1601 | 0.8049 | |
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| 0.0116 | 22.02 | 1863 | 1.3358 | 0.7073 | |
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| 0.015 | 23.02 | 1944 | 1.0973 | 0.8049 | |
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| 0.1993 | 24.02 | 2025 | 1.2985 | 0.7805 | |
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| 0.1496 | 25.02 | 2106 | 1.1559 | 0.7805 | |
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| 0.2059 | 26.02 | 2187 | 1.5124 | 0.7561 | |
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| 0.0176 | 27.02 | 2268 | 1.4691 | 0.7805 | |
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| 0.2477 | 28.02 | 2349 | 1.5908 | 0.7805 | |
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| 0.0007 | 29.02 | 2430 | 1.3929 | 0.7805 | |
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| 0.0003 | 30.02 | 2511 | 1.3020 | 0.7805 | |
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| 0.0002 | 31.02 | 2592 | 1.5539 | 0.7805 | |
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| 0.0001 | 32.02 | 2673 | 1.5173 | 0.8049 | |
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| 0.0001 | 33.02 | 2754 | 1.6113 | 0.8049 | |
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| 0.0918 | 34.02 | 2835 | 1.5837 | 0.7805 | |
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| 0.0001 | 35.02 | 2916 | 1.4928 | 0.8049 | |
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| 0.0001 | 36.02 | 2997 | 1.4138 | 0.8049 | |
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| 0.2197 | 37.02 | 3078 | 1.3980 | 0.8049 | |
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| 0.0001 | 38.02 | 3159 | 1.6004 | 0.8049 | |
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| 0.0002 | 39.02 | 3240 | 1.5590 | 0.8049 | |
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| 0.0246 | 40.02 | 3321 | 1.5889 | 0.7805 | |
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| 0.0002 | 41.02 | 3402 | 1.6164 | 0.7805 | |
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| 0.0001 | 42.02 | 3483 | 1.6072 | 0.7805 | |
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| 0.0001 | 43.02 | 3564 | 1.6259 | 0.7805 | |
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| 0.0001 | 44.02 | 3645 | 1.5990 | 0.7805 | |
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| 0.0002 | 45.02 | 3726 | 1.5517 | 0.7805 | |
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| 0.0 | 46.02 | 3807 | 1.5260 | 0.7805 | |
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| 0.0146 | 47.02 | 3888 | 1.5292 | 0.7805 | |
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| 0.0001 | 48.02 | 3969 | 1.5405 | 0.7805 | |
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| 0.0001 | 49.01 | 4000 | 1.5414 | 0.7805 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 3.0.1 |
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- Tokenizers 0.15.1 |
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