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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: 0.6543 Accuracy: 0.8503
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 12 sept.
Artefacts du modèle
1 artefactExtraits de sources
2 extraitsMore information needed
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3887 | 0.1 | 288 | 0.6569 | 0.8110 |
| 0.0345 | 1.1 | 576 | 0.8916 | 0.8496 |
| 0.0117 | 2.1 | 864 | 1.0667 | 0.8374 |
| 0.0001 | 3.1 | 1152 | 1.5495 | 0.7764 |
--- 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: ALL_RGBCROP_Aug16F-8B16F 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. --> # ALL_RGBCROP_Aug16F-8B16F 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: 0.6543 - Accuracy: 0.8503 ## 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 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: 2880 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3887 | 0.1 | 288 | 0.6569 | 0.8110 | | 0.0345 | 1.1 | 576 | 0.8916 | 0.8496 | | 0.0117 | 2.1 | 864 | 1.0667 | 0.8374 | | 0.0001 | 3.1 | 1152 | 1.5495 | 0.7764 | ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
Source context: 2 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo TanAlexanderlz/ALL_RGBCROP_Aug16F-8B16F
