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--- |
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license: cc-by-nc-4.0 |
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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-short-finetuned-kinetics-finetuned-rwf2000-epochs8-batch8-k |
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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-short-finetuned-kinetics-finetuned-rwf2000-epochs8-batch8-k |
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This model is a fine-tuned version of [MCG-NJU/videomae-base-short-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-short-finetuned-kinetics) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0855 |
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- Accuracy: 0.6764 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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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: 3200 |
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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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| 0.3699 | 0.06 | 200 | 0.2920 | 0.8925 | |
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| 0.3127 | 1.06 | 400 | 0.6035 | 0.77 | |
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| 0.2864 | 2.06 | 600 | 0.5088 | 0.8237 | |
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| 0.1739 | 3.06 | 800 | 0.6310 | 0.765 | |
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| 0.1835 | 4.06 | 1000 | 0.3643 | 0.8738 | |
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| 0.1075 | 5.06 | 1200 | 0.3455 | 0.8862 | |
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| 0.1362 | 6.06 | 1400 | 0.9145 | 0.75 | |
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| 0.2958 | 7.06 | 1600 | 0.3544 | 0.895 | |
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| 0.1571 | 8.06 | 1800 | 0.3748 | 0.8912 | |
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| 0.2448 | 9.06 | 2000 | 0.3646 | 0.8975 | |
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| 0.1939 | 10.06 | 2200 | 0.4430 | 0.8762 | |
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| 0.0666 | 11.06 | 2400 | 0.4916 | 0.8762 | |
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| 0.1958 | 12.06 | 2600 | 0.5114 | 0.8638 | |
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| 0.1063 | 13.06 | 2800 | 0.5701 | 0.8612 | |
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| 0.1047 | 14.06 | 3000 | 0.5226 | 0.8688 | |
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| 0.0131 | 15.06 | 3200 | 0.4656 | 0.8812 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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