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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: 0.3212 Accuracy: 0.9102
Source du modèle
Description de la source
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 sourceVérifié 18 sept.
Artefacts du modèle
1 artefactExtraits de sources
2 extraitsMore information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0467 | 0.01 | 687 | 0.2513 | 0.9300 |
| 0.0084 | 1.01 | 1374 | 0.4972 | 0.8741 |
| 0.0529 | 2.01 | 2061 | 0.2346 | 0.9045 |
| 0.6585 | 3.01 | 2748 | 0.6326 | 0.8165 |
| 0.6976 | 4.01 | 3435 | 0.7723 | 0.8165 |
--- 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-0325_final 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-0325_final 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: 0.3212 - Accuracy: 0.9102 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0467 | 0.01 | 687 | 0.2513 | 0.9300 | | 0.0084 | 1.01 | 1374 | 0.4972 | 0.8741 | | 0.0529 | 2.01 | 2061 | 0.2346 | 0.9045 | | 0.6585 | 3.01 | 2748 | 0.6326 | 0.8165 | | 0.6976 | 4.01 | 3435 | 0.7723 | 0.8165 | | 0.5654 | 5.01 | 4122 | 0.5031 | 0.8593 | | 0.2582 | 6.01 | 4809 | 1.0343 | 0.7720 | | 0.6946 | 7.01 | 5496 | 0.9468 | 0.7975 | | 0.0285 | 8.01 | 6183 | 0.5327 | 0.8370 | | 0.4778 | 9.01 | 6870 | 1.1722 | 0.7292 | | 0.955 | 10.01 | 7557 | 0.6246 | 0.8609 | | 0.3365 | 11.01 | 8244 | 0.9516 | 0.7728 | | 0.0242 | 12.01 | 8931 | 0.6680 | 0.8354 | | 0.338 | 13.01 | 9618 | 1.0590 | 0.8066 | | 0.4445 | 14.01 | 10305 | 0.4882 | 0.8872 | | 0.294 | 15.01 | 10992 | 1.1611 | 0.7350 | ### 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-0325_final
| 0.5654 | 5.01 | 4122 | 0.5031 | 0.8593 |
| 0.2582 | 6.01 | 4809 | 1.0343 | 0.7720 |
| 0.6946 | 7.01 | 5496 | 0.9468 | 0.7975 |
| 0.0285 | 8.01 | 6183 | 0.5327 | 0.8370 |
| 0.4778 | 9.01 | 6870 | 1.1722 | 0.7292 |
| 0.955 | 10.01 | 7557 | 0.6246 | 0.8609 |
| 0.3365 | 11.01 | 8244 | 0.9516 | 0.7728 |
| 0.0242 | 12.01 | 8931 | 0.6680 | 0.8354 |
| 0.338 | 13.01 | 9618 | 1.0590 | 0.8066 |
| 0.4445 | 14.01 | 10305 | 0.4882 | 0.8872 |
| 0.294 | 15.01 | 10992 | 1.1611 | 0.7350 |
