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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: 0.6152 Accuracy: 0.8422
Modellquelle
Quellenbeschreibung
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
Quellen
1 QuelleVerifiziert 18. Sept.
Modellartefakte
1 ArtefaktQuellenauszüge
2 AuszügeMore information needed
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0032 | 0.01 | 687 | 0.8430 | 0.8112 |
| 0.0067 | 1.01 | 1374 | 0.8257 | 0.7844 |
| 0.0193 | 2.01 | 2061 | 0.3668 | 0.8720 |
| 0.1767 | 3.01 | 2748 | 0.7760 | 0.7966 |
| 1.1229 | 4.01 | 3435 | 0.7641 | 0.8079 |
--- 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_patience 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_patience 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: 0.6152 - Accuracy: 0.8422 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0032 | 0.01 | 687 | 0.8430 | 0.8112 | | 0.0067 | 1.01 | 1374 | 0.8257 | 0.7844 | | 0.0193 | 2.01 | 2061 | 0.3668 | 0.8720 | | 0.1767 | 3.01 | 2748 | 0.7760 | 0.7966 | | 1.1229 | 4.01 | 3435 | 0.7641 | 0.8079 | | 0.0123 | 5.01 | 4122 | 0.3614 | 0.8825 | | 0.0299 | 6.01 | 4809 | 1.0045 | 0.7593 | | 0.31 | 7.01 | 5496 | 0.8982 | 0.8290 | | 0.0079 | 8.01 | 6183 | 0.6962 | 0.8525 | | 0.2623 | 9.01 | 6870 | 1.7303 | 0.6807 | | 0.4076 | 10.01 | 7557 | 0.9215 | 0.7998 | | 0.1049 | 11.01 | 8244 | 1.0585 | 0.7966 | | 0.0079 | 12.01 | 8931 | 1.0430 | 0.8039 | | 0.0366 | 13.01 | 9618 | 0.6344 | 0.8590 | | 0.0007 | 14.01 | 10305 | 0.8699 | 0.8379 | | 0.2756 | 15.01 | 10992 | 0.9275 | 0.8217 | ### Framework versions - Transformers 4.48.0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
Source context: 15 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo d2o2ji/videomae-base-finetuned-kinetics-0326_final_patience
| 0.0123 | 5.01 | 4122 | 0.3614 | 0.8825 |
| 0.0299 | 6.01 | 4809 | 1.0045 | 0.7593 |
| 0.31 | 7.01 | 5496 | 0.8982 | 0.8290 |
| 0.0079 | 8.01 | 6183 | 0.6962 | 0.8525 |
| 0.2623 | 9.01 | 6870 | 1.7303 | 0.6807 |
| 0.4076 | 10.01 | 7557 | 0.9215 | 0.7998 |
| 0.1049 | 11.01 | 8244 | 1.0585 | 0.7966 |
| 0.0079 | 12.01 | 8931 | 1.0430 | 0.8039 |
| 0.0366 | 13.01 | 9618 | 0.6344 | 0.8590 |
| 0.0007 | 14.01 | 10305 | 0.8699 | 0.8379 |
| 0.2756 | 15.01 | 10992 | 0.9275 | 0.8217 |
