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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: 1.5308 Accuracy: 0.6605
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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 |
|---|---|---|---|---|
| 1.6249 | 0.1 | 625 | 1.7054 | 0.3542 |
| 1.3162 | 1.1 | 1250 | 1.6491 | 0.4539 |
| 1.575 | 2.1 | 1875 | 1.4801 | 0.4834 |
| 1.4376 | 3.1 | 2500 | 1.4187 | 0.5351 |
| 0.935 | 4.1 | 3125 | 1.5090 | 0.5129 |
--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: cricketshot-predictor 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. --> # cricketshot-predictor 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: 1.5308 - Accuracy: 0.6605 ## 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: 2 - eval_batch_size: 2 - 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: 6250 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.6249 | 0.1 | 625 | 1.7054 | 0.3542 | | 1.3162 | 1.1 | 1250 | 1.6491 | 0.4539 | | 1.575 | 2.1 | 1875 | 1.4801 | 0.4834 | | 1.4376 | 3.1 | 2500 | 1.4187 | 0.5351 | | 0.935 | 4.1 | 3125 | 1.5090 | 0.5129 | | 0.8346 | 5.1 | 3750 | 1.2971 | 0.6089 | | 0.8204 | 6.1 | 4375 | 1.7406 | 0.5609 | | 0.2341 | 7.1 | 5000 | 1.5780 | 0.6236 | | 0.8821 | 8.1 | 5625 | 1.6022 | 0.6310 | | 0.2366 | 9.1 | 6250 | 1.5308 | 0.6605 | ### Framework versions - Transformers 4.49.0 - Pytorch 2.5.1 - Datasets 3.3.2 - Tokenizers 0.21.0
Source context: 14 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo rokmr/cricketshot-predictor
| 0.8346 | 5.1 | 3750 | 1.2971 | 0.6089 |
| 0.8204 | 6.1 | 4375 | 1.7406 | 0.5609 |
| 0.2341 | 7.1 | 5000 | 1.5780 | 0.6236 |
| 0.8821 | 8.1 | 5625 | 1.6022 | 0.6310 |
| 0.2366 | 9.1 | 6250 | 1.5308 | 0.6605 |
