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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: 2.2585 Accuracy: 0.2
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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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1 fuenteVerificado 6 ago
Artefactos del modelo
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.3151 | 0.275 | 11 | 2.2927 | 0.1381 |
| 2.3125 | 1.275 | 22 | 2.2638 | 0.1667 |
| 2.2763 | 2.275 | 33 | 2.2605 | 0.1429 |
| 2.2415 | 3.175 | 40 | 2.2585 | 0.2 |
--- 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: 2.2585 - Accuracy: 0.2 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.3151 | 0.275 | 11 | 2.2927 | 0.1381 | | 2.3125 | 1.275 | 22 | 2.2638 | 0.1667 | | 2.2763 | 2.275 | 33 | 2.2605 | 0.1429 | | 2.2415 | 3.175 | 40 | 2.2585 | 0.2 | ### Framework versions - Transformers 4.57.3 - Pytorch 2.9.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.1
Source context: 8 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo sano90/videomae-base-finetuned-ucf101-subset
