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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: facebook/timesformer-base-finetuned-k400 |
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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: vivit-timesformer-d1 |
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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-timesformer-d1 |
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This model is a fine-tuned version of [facebook/timesformer-base-finetuned-k400](https://huggingface.co/facebook/timesformer-base-finetuned-k400) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4413 |
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- Accuracy: 0.7876 |
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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-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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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_ratio: 0.1 |
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- training_steps: 12010 |
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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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| 1.4177 | 0.1 | 1201 | 1.6974 | 0.6286 | |
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| 1.3055 | 1.1 | 2402 | 1.7565 | 0.6570 | |
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| 0.8467 | 2.1 | 3603 | 1.7238 | 0.6763 | |
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| 0.0019 | 3.1 | 4804 | 1.6070 | 0.6877 | |
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| 1.0625 | 4.1 | 6005 | 1.6971 | 0.6939 | |
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| 0.0012 | 5.1 | 7206 | 1.8048 | 0.7005 | |
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| 0.8867 | 6.1 | 8407 | 1.6243 | 0.7337 | |
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| 1.7533 | 7.1 | 9608 | 1.3174 | 0.7661 | |
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| 0.8878 | 8.1 | 10809 | 1.5021 | 0.7828 | |
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| 0.0005 | 9.1 | 12010 | 1.4413 | 0.7876 | |
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### Framework versions |
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- Transformers 4.46.2 |
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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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