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--- |
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library_name: transformers |
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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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- precision |
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- recall |
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model-index: |
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- name: videomae-base-finetuned-ucf-crimevbinary-balanced-vwandb |
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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-ucf-crimevbinary-balanced-vwandb |
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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: 0.2106 |
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- Accuracy: 0.9444 |
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- Precision: 0.9444 |
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- Recall: 0.9444 |
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- Auc: 0.9815 |
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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: 3e-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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.6542 | 1.0 | 29 | 0.6413 | 0.6389 | 0.7217 | 0.6389 | 0.8056 | |
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| 0.574 | 2.0 | 58 | 0.5412 | 0.75 | 0.7945 | 0.75 | 0.8827 | |
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| 0.4214 | 3.0 | 87 | 0.4414 | 0.8611 | 0.8714 | 0.8611 | 0.9228 | |
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| 0.4331 | 4.0 | 116 | 0.4604 | 0.8056 | 0.8311 | 0.8056 | 0.8889 | |
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| 0.2981 | 5.0 | 145 | 0.3923 | 0.8889 | 0.8937 | 0.8889 | 0.9321 | |
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| 0.2615 | 6.0 | 174 | 0.5136 | 0.8333 | 0.8506 | 0.8333 | 0.8735 | |
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| 0.1896 | 7.0 | 203 | 0.4989 | 0.8889 | 0.9091 | 0.8889 | 0.9105 | |
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| 0.5031 | 8.0 | 232 | 0.3814 | 0.9167 | 0.9180 | 0.9167 | 0.9321 | |
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| 0.0947 | 9.0 | 261 | 0.4635 | 0.8889 | 0.8937 | 0.8889 | 0.9290 | |
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| 0.3865 | 10.0 | 290 | 0.5199 | 0.8889 | 0.9091 | 0.8889 | 0.8951 | |
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| 0.187 | 11.0 | 319 | 0.6748 | 0.8611 | 0.8622 | 0.8611 | 0.9136 | |
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| 0.0408 | 12.0 | 348 | 1.0193 | 0.8056 | 0.8143 | 0.8056 | 0.9012 | |
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| 0.3851 | 13.0 | 377 | 0.3708 | 0.8889 | 0.8889 | 0.8889 | 0.9630 | |
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### Framework versions |
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- Transformers 4.47.0.dev0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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