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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: 0.2570 Accuracy: 0.9538
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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 18 de set.
Artefatos de modelo
1 artefatoTrechos de fonte
2 trechosMore information needed
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.397 | 0.1 | 903 | 0.3337 | 0.9478 |
| 0.1855 | 1.1001 | 1807 | 0.0722 | 0.9801 |
| 0.1028 | 2.1 | 2710 | 0.0458 | 0.9838 |
| 0.0946 | 3.1001 | 3614 | 0.0658 | 0.9851 |
| 0.2243 | 4.1 | 4517 | 0.0119 | 0.9950 |
--- 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_default_fold__0__10_epoch_Aug_batch_2_4_BdSLW60_SR_8_p2_kineticsFinetuned_18_25 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_default_fold__0__10_epoch_Aug_batch_2_4_BdSLW60_SR_8_p2_kineticsFinetuned_18_25 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: 0.2570 - Accuracy: 0.9538 ## 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: 9030 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 1.397 | 0.1 | 903 | 0.3337 | 0.9478 | | 0.1855 | 1.1001 | 1807 | 0.0722 | 0.9801 | | 0.1028 | 2.1 | 2710 | 0.0458 | 0.9838 | | 0.0946 | 3.1001 | 3614 | 0.0658 | 0.9851 | | 0.2243 | 4.1 | 4517 | 0.0119 | 0.9950 | | 0.0173 | 5.1001 | 5421 | 0.0631 | 0.9851 | | 0.0016 | 6.1 | 6324 | 0.0287 | 0.9925 | | 0.0068 | 7.1001 | 7228 | 0.0206 | 0.9963 | | 0.0253 | 8.1 | 8131 | 0.0121 | 0.9963 | | 0.0027 | 9.0995 | 9030 | 0.0117 | 0.9988 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.1
Source context: 2 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/VideoMAE_default_fold__0__10_epoch_Aug_batch_2_4_BdSLW60_SR_8_p2_kineticsFinetuned_18_25
| 0.0173 | 5.1001 | 5421 | 0.0631 | 0.9851 |
| 0.0016 | 6.1 | 6324 | 0.0287 | 0.9925 |
| 0.0068 | 7.1001 | 7228 | 0.0206 | 0.9963 |
| 0.0253 | 8.1 | 8131 | 0.0121 | 0.9963 |
| 0.0027 | 9.0995 | 9030 | 0.0117 | 0.9988 |
