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This model is a fine-tuned version of MCG-NJU/videomae-base-finetuned-kinetics on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.2486 Accuracy: 0.9491
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This model is a fine-tuned version of MCG-NJU/videomae-base-finetuned-kinetics 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 |
|---|---|---|---|---|
| 1.462 | 0.1 | 903 | 0.3206 | 0.9489 |
| 0.0928 | 1.1001 | 1807 | 0.0966 | 0.9826 |
| 0.0158 | 2.1 | 2710 | 0.0369 | 0.9913 |
| 0.0873 | 3.1001 | 3614 | 0.0459 | 0.9900 |
| 0.0721 | 4.1 | 4517 | 0.0413 | 0.9913 |
--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: VideoMAE_default_fold__2__10_epoch_bdslw60_kinetics_25_2_25 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_default_fold__2__10_epoch_bdslw60_kinetics_25_2_25 This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2486 - Accuracy: 0.9491 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - 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: 9030 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 1.462 | 0.1 | 903 | 0.3206 | 0.9489 | | 0.0928 | 1.1001 | 1807 | 0.0966 | 0.9826 | | 0.0158 | 2.1 | 2710 | 0.0369 | 0.9913 | | 0.0873 | 3.1001 | 3614 | 0.0459 | 0.9900 | | 0.0721 | 4.1 | 4517 | 0.0413 | 0.9913 | | 0.0148 | 5.1001 | 5421 | 0.0440 | 0.9913 | | 0.0266 | 6.1 | 6324 | 0.0333 | 0.9938 | | 0.0007 | 7.1001 | 7228 | 0.0340 | 0.9938 | | 0.0005 | 8.1 | 8131 | 0.0465 | 0.9913 | | 0.0089 | 9.0995 | 9030 | 0.0239 | 0.9950 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.1
Source context: 3 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/VideoMAE_default_fold__2__10_epoch_bdslw60_kinetics_25_2_25
| 0.0148 | 5.1001 | 5421 | 0.0440 | 0.9913 |
| 0.0266 | 6.1 | 6324 | 0.0333 | 0.9938 |
| 0.0007 | 7.1001 | 7228 | 0.0340 | 0.9938 |
| 0.0005 | 8.1 | 8131 | 0.0465 | 0.9913 |
| 0.0089 | 9.0995 | 9030 | 0.0239 | 0.9950 |
