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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-crimevbinaryv4 |
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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-crimevbinaryv4 |
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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: 0.8055 |
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- Accuracy: 0.8571 |
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- Precision: 0.8571 |
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- Recall: 0.8571 |
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- Auc: 0.9328 |
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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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| 0.4783 | 1.0 | 175 | 0.5235 | 0.78 | 0.7825 | 0.78 | 0.8269 | |
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| 0.6121 | 2.0 | 350 | 0.5076 | 0.816 | 0.8179 | 0.816 | 0.8518 | |
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| 0.3305 | 3.0 | 525 | 0.3910 | 0.856 | 0.8660 | 0.856 | 0.9222 | |
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| 0.2431 | 4.0 | 700 | 0.8684 | 0.728 | 0.8190 | 0.728 | 0.8933 | |
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| 0.3337 | 5.0 | 875 | 0.5653 | 0.808 | 0.8266 | 0.808 | 0.9149 | |
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| 0.1768 | 6.0 | 1050 | 0.4709 | 0.88 | 0.8803 | 0.88 | 0.9311 | |
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| 0.111 | 7.0 | 1225 | 0.6650 | 0.848 | 0.8600 | 0.848 | 0.9263 | |
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| 0.1507 | 8.0 | 1400 | 0.6151 | 0.852 | 0.8522 | 0.852 | 0.9232 | |
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| 0.1771 | 9.0 | 1575 | 0.6924 | 0.872 | 0.8729 | 0.872 | 0.9280 | |
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| 0.13 | 10.0 | 1750 | 0.6670 | 0.884 | 0.8846 | 0.884 | 0.9304 | |
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| 0.0062 | 11.0 | 1925 | 0.8110 | 0.86 | 0.8621 | 0.86 | 0.9198 | |
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| 0.0008 | 12.0 | 2100 | 0.8139 | 0.876 | 0.8768 | 0.876 | 0.9235 | |
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| 0.0004 | 13.0 | 2275 | 0.8727 | 0.868 | 0.8711 | 0.868 | 0.9258 | |
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| 0.0005 | 14.0 | 2450 | 1.0055 | 0.86 | 0.8658 | 0.86 | 0.9250 | |
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| 0.0002 | 15.0 | 2625 | 0.9728 | 0.864 | 0.8677 | 0.864 | 0.9269 | |
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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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