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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: 0.5436 Accuracy: 0.8286
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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 |
|---|---|---|---|---|
| 1.9717 | 0.25 | 30 | 1.8129 | 0.5286 |
| 0.9709 | 1.25 | 60 | 1.0309 | 0.6429 |
| 0.5511 | 2.25 | 90 | 0.6306 | 0.7714 |
| 0.3458 | 3.25 | 120 | 0.5436 | 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: 0.5436 - 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: 10 - eval_batch_size: 10 - 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: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9717 | 0.25 | 30 | 1.8129 | 0.5286 | | 0.9709 | 1.25 | 60 | 1.0309 | 0.6429 | | 0.5511 | 2.25 | 90 | 0.6306 | 0.7714 | | 0.3458 | 3.25 | 120 | 0.5436 | 0.8286 | ### Framework versions - Transformers 4.50.2 - Pytorch 2.1.0 - Datasets 3.5.0 - Tokenizers 0.21.1
Source context: 4 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo cwang138/videomae-base-finetuned-ucf101-subset
