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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: 1.0012 Accuracy: 0.6828
Model source
Source description
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:
More information needed
Sources
1 sourceVerified Sep 18
Model artifacts
1 artifactSource excerpts
2 excerptsMore information needed
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2669 | 0.01 | 687 | 0.7324 | 0.7318 |
| 0.311 | 1.01 | 1374 | 1.2855 | 0.6386 |
| 0.1627 | 2.01 | 2061 | 0.7297 | 0.7788 |
| 0.4673 | 3.01 | 2748 | 1.7532 | 0.6191 |
| 0.9694 | 4.01 | 3435 | 0.9305 | 0.7715 |
--- 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-base-finetuned-kinetics-0326_final_imgsz256 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-kinetics-0326_final_imgsz256 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: 1.0012 - Accuracy: 0.6828 ## 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 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: 0.05 - training_steps: 68700 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2669 | 0.01 | 687 | 0.7324 | 0.7318 | | 0.311 | 1.01 | 1374 | 1.2855 | 0.6386 | | 0.1627 | 2.01 | 2061 | 0.7297 | 0.7788 | | 0.4673 | 3.01 | 2748 | 1.7532 | 0.6191 | | 0.9694 | 4.01 | 3435 | 0.9305 | 0.7715 | | 0.2372 | 5.01 | 4122 | 1.0085 | 0.7423 | | 0.3608 | 6.01 | 4809 | 0.7486 | 0.7618 | | 0.8374 | 7.01 | 5496 | 1.1176 | 0.7366 | | 0.0445 | 8.01 | 6183 | 1.2131 | 0.7382 | | 0.6795 | 9.01 | 6870 | 1.7516 | 0.6345 | | 0.2234 | 10.01 | 7557 | 2.2503 | 0.5989 | | 0.0306 | 11.01 | 8244 | 1.5213 | 0.7115 | | 0.1396 | 12.01 | 8931 | 1.3085 | 0.7253 | ### Framework versions - Transformers 4.48.0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
Source context: 4 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo d2o2ji/videomae-base-finetuned-kinetics-0326_final_imgsz256
| 0.2372 | 5.01 | 4122 | 1.0085 | 0.7423 |
| 0.3608 | 6.01 | 4809 | 0.7486 | 0.7618 |
| 0.8374 | 7.01 | 5496 | 1.1176 | 0.7366 |
| 0.0445 | 8.01 | 6183 | 1.2131 | 0.7382 |
| 0.6795 | 9.01 | 6870 | 1.7516 | 0.6345 |
| 0.2234 | 10.01 | 7557 | 2.2503 | 0.5989 |
| 0.0306 | 11.01 | 8244 | 1.5213 | 0.7115 |
| 0.1396 | 12.01 | 8931 | 1.3085 | 0.7253 |
