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--- |
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license: mit |
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base_model: google/vivit-b-16x2-kinetics400 |
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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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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: vivit-b-16x2-kinetics400-finetuned-cctv-surveillance |
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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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# vivit-b-16x2-kinetics400-finetuned-cctv-surveillance |
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This model is a fine-tuned version of [google/vivit-b-16x2-kinetics400](https://huggingface.co/google/vivit-b-16x2-kinetics400) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1690 |
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- Accuracy: 0.9559 |
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- F1: 0.9430 |
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- Recall: 0.9559 |
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- Precision: 0.9333 |
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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: 5e-06 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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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: 4032 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 1.5836 | 0.12 | 504 | 0.3644 | 0.9206 | 0.8850 | 0.9206 | 0.8799 | |
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| 0.3767 | 1.12 | 1008 | 0.2586 | 0.9265 | 0.8994 | 0.9265 | 0.8831 | |
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| 0.2063 | 2.12 | 1512 | 0.2190 | 0.9294 | 0.9097 | 0.9294 | 0.9002 | |
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| 0.4514 | 3.12 | 2016 | 0.2217 | 0.9529 | 0.9419 | 0.9529 | 0.9380 | |
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| 0.2678 | 4.12 | 2520 | 0.1919 | 0.9529 | 0.9419 | 0.9529 | 0.9380 | |
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| 0.2311 | 5.12 | 3024 | 0.1797 | 0.9412 | 0.9252 | 0.9412 | 0.9141 | |
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| 0.5256 | 6.12 | 3528 | 0.1690 | 0.9559 | 0.9430 | 0.9559 | 0.9333 | |
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| 0.539 | 7.12 | 4032 | 0.1678 | 0.9529 | 0.9398 | 0.9529 | 0.9297 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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