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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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