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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: 0.5117 Accuracy: 0.8286
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Quellenbeschreibung
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 QuelleVerifiziert 6. Aug.
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.1418 | 0.2568 | 38 | 1.8546 | 0.3857 |
| 1.1493 | 1.2568 | 76 | 1.0060 | 0.6143 |
| 0.5133 | 2.2568 | 114 | 0.6389 | 0.8 |
| 0.3043 | 3.2297 | 148 | 0.5117 | 0.8286 |
--- 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: 0.5117 - Accuracy: 0.8286 ## 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_steps: 0.1 - training_steps: 148 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 2.1418 | 0.2568 | 38 | 1.8546 | 0.3857 | | 1.1493 | 1.2568 | 76 | 1.0060 | 0.6143 | | 0.5133 | 2.2568 | 114 | 0.6389 | 0.8 | | 0.3043 | 3.2297 | 148 | 0.5117 | 0.8286 | ### Framework versions - Transformers 5.0.0 - Pytorch 2.10.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2
Source context: 5 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo erdoganeray/videomae-base-finetuned-ucf101-subset
