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--- |
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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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model-index: |
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- name: videomae-base-finetuned-fight-nofight-subset2 |
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results: [] |
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datasets: |
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- Pinwheel/ActsOfAgression |
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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-fight-nofight-subset2 |
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**NOTE: This is experimentational if youre expecting this to work accurately (it wont) or be useful should probably look eslewhere😛** |
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This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on the [Acts of Agression (cttv footage fights)](https://huggingface.co/datasets/Pinwheel/ActsOfAgression) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5190 |
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- Accuracy: 0.7435 |
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## Model description |
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Classifies video input into "Fight" or "No Fight" Class |
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## Intended uses & limitations |
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Can be used to detect fights/crime in cctv footage |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-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: 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: 252 |
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- mixed_precision_training: Native AMP |
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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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| 0.5145 | 0.25 | 64 | 0.7845 | 0.5075 | |
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| 0.607 | 1.25 | 128 | 0.6886 | 0.6343 | |
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| 0.3986 | 2.25 | 192 | 0.5106 | 0.7463 | |
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| 0.3632 | 3.24 | 252 | 0.7408 | 0.6716 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2 |
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- Datasets 2.1.0 |
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- Tokenizers 0.15.1 |
