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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: 0.0005 Accuracy: 1.0 Precision: 1.0 Recall: 1.0 F1: 1.0
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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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1 fonteVerificado 21 de ago.
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 14.4931 | 0.05 | 288 | 2.1667 | 0.7617 | 0.7968 | 0.7617 | 0.7337 |
| 2.5661 | 1.0500 | 576 | 0.2914 | 0.9805 | 0.9734 | 0.9805 | 0.9752 |
| 0.27 | 2.0500 | 864 | 0.0165 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0167 |
--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: VideoMAE_LSA64SR_12_kineticsFinetuned 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_LSA64SR_12_kineticsFinetuned 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: 0.0005 - Accuracy: 1.0 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 ## 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: 5760 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 14.4931 | 0.05 | 288 | 2.1667 | 0.7617 | 0.7968 | 0.7617 | 0.7337 | | 2.5661 | 1.0500 | 576 | 0.2914 | 0.9805 | 0.9734 | 0.9805 | 0.9752 | | 0.27 | 2.0500 | 864 | 0.0165 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0167 | 3.0500 | 1152 | 0.0026 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.037 | 4.0500 | 1440 | 0.0018 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.045 | 5.0500 | 1728 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.006 | 6.0500 | 2016 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | | 0.0022 | 7.0500 | 2304 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.1
Source context: 9 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/VideoMAE_LSA64SR_8_kineticsFinetuned
| 3.0500 |
| 1152 |
| 0.0026 |
| 1.0 |
| 1.0 |
| 1.0 |
| 1.0 |
| 0.037 | 4.0500 | 1440 | 0.0018 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.045 | 5.0500 | 1728 | 0.0008 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.006 | 6.0500 | 2016 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0022 | 7.0500 | 2304 | 0.0005 | 1.0 | 1.0 | 1.0 | 1.0 |
