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This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.3898 Accuracy: 0.9143
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This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.6092 | 0.25 | 300 | 1.5911 | 0.3571 |
| 1.6975 | 1.25 | 600 | 1.5007 | 0.5714 |
| 0.3291 | 2.25 | 900 | 0.8403 | 0.8143 |
| 1.1484 | 3.25 | 1200 | 0.3898 | 0.9143 |
--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3898 - Accuracy: 0.9143 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6092 | 0.25 | 300 | 1.5911 | 0.3571 | | 1.6975 | 1.25 | 600 | 1.5007 | 0.5714 | | 0.3291 | 2.25 | 900 | 0.8403 | 0.8143 | | 1.1484 | 3.25 | 1200 | 0.3898 | 0.9143 | ### Framework versions - Transformers 4.46.3 - Pytorch 2.1.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
Source context: 2 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo iamliguangming/videomae-base-finetuned-ucf101-subset
