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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: 0.8283 Accuracy: 0.8143
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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 |
|---|---|---|---|---|
| 2.3367 | 0.2639 | 19 | 1.9821 | 0.4714 |
| 1.8478 | 1.2639 | 38 | 1.3762 | 0.6 |
| 1.1441 | 2.2639 | 57 | 0.9456 | 0.7429 |
| 0.6985 | 3.2083 | 72 | 0.8283 | 0.8143 |
--- 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-subset1 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-subset1 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: 0.8283 - Accuracy: 0.8143 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.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: 72 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 2.3367 | 0.2639 | 19 | 1.9821 | 0.4714 | | 1.8478 | 1.2639 | 38 | 1.3762 | 0.6 | | 1.1441 | 2.2639 | 57 | 0.9456 | 0.7429 | | 0.6985 | 3.2083 | 72 | 0.8283 | 0.8143 | ### Framework versions - Transformers 4.50.1 - Pytorch 2.6.0+cu124 - Datasets 3.4.1 - Tokenizers 0.21.1
Source context: 3 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo ffstca/videomae-base-finetuned-ucf101-subset1
