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: 0.2367 Accuracy: 0.9553
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:
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Sources
1 sourceVérifié 18 sept.
Artefacts du modèle
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0666 | 0.01 | 350 | 0.6833 | 0.7785 |
| 0.2426 | 1.01 | 700 | 0.3021 | 0.8939 |
| 0.2266 | 2.01 | 1050 | 0.3887 | 0.8621 |
| 0.1474 | 3.01 | 1400 | 0.3437 | 0.8660 |
| 0.0026 | 4.01 | 1750 | 0.4075 | 0.8727 |
--- 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-0313-clip_duration-abnormal02 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-0313-clip_duration-abnormal02 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: 0.2367 - Accuracy: 0.9553 ## 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: 35000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0666 | 0.01 | 350 | 0.6833 | 0.7785 | | 0.2426 | 1.01 | 700 | 0.3021 | 0.8939 | | 0.2266 | 2.01 | 1050 | 0.3887 | 0.8621 | | 0.1474 | 3.01 | 1400 | 0.3437 | 0.8660 | | 0.0026 | 4.01 | 1750 | 0.4075 | 0.8727 | | 0.0385 | 5.01 | 2100 | 0.3817 | 0.8979 | | 0.0054 | 6.01 | 2450 | 0.5044 | 0.8793 | | 0.0002 | 7.01 | 2800 | 0.6241 | 0.8714 | | 0.0155 | 8.01 | 3150 | 0.5603 | 0.8820 | | 0.31 | 9.01 | 3500 | 0.4432 | 0.8992 | | 0.0004 | 10.01 | 3850 | 0.4870 | 0.8899 | | 0.0005 | 11.01 | 4200 | 0.5764 | 0.8979 | | 0.0321 | 12.01 | 4550 | 0.4764 | 0.9032 | | 0.0001 | 13.01 | 4900 | 0.5965 | 0.8966 | | 0.0004 | 14.01 | 5250 | 0.5833 | 0.8939 | | 0.0001 | 15.01 | 5600 | 0.6168 | 0.8833 | | 0.0085 | 16.01 | 5950 | 0.5132 | 0.9072 | | 0.0 | 17.01 | 6300 | 0.5239 | 0.9045 | | 0.1187 | 18.01 | 6650 | 0.5492 | 0.8926 | | 0.0001 | 19.01 | 7000 | 0.6863 | 0.8793 | | 0.0001 | 20.01 | 7350 | 0.7093 | 0.8859 | | 0.0 | 21....
Source context: 4 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo d2o2ji/videomae-base-finetuned-kinetics-0313-clip_duration-abnormal02
| 0.0385 | 5.01 | 2100 | 0.3817 | 0.8979 |
| 0.0054 | 6.01 | 2450 | 0.5044 | 0.8793 |
| 0.0002 | 7.01 | 2800 | 0.6241 | 0.8714 |
| 0.0155 | 8.01 | 3150 | 0.5603 | 0.8820 |
| 0.31 | 9.01 | 3500 | 0.4432 | 0.8992 |
| 0.0004 | 10.01 | 3850 | 0.4870 | 0.8899 |
| 0.0005 | 11.01 | 4200 | 0.5764 | 0.8979 |
| 0.0321 | 12.01 | 4550 | 0.4764 | 0.9032 |
| 0.0001 | 13.01 | 4900 | 0.5965 | 0.8966 |
| 0.0004 | 14.01 | 5250 | 0.5833 | 0.8939 |
| 0.0001 | 15.01 | 5600 | 0.6168 | 0.8833 |
| 0.0085 | 16.01 | 5950 | 0.5132 | 0.9072 |
| 0.0 | 17.01 | 6300 | 0.5239 | 0.9045 |
| 0.1187 | 18.01 | 6650 | 0.5492 | 0.8926 |
| 0.0001 | 19.01 | 7000 | 0.6863 | 0.8793 |
| 0.0001 | 20.01 | 7350 | 0.7093 | 0.8859 |
| 0.0 | 21.01 | 7700 | 0.5853 | 0.8912 |
| 0.0 | 22.01 | 8050 | 0.5131 | 0.9045 |
| 0.0 | 23.01 | 8400 | 0.6446 | 0.8912 |
| 0.0 | 24.01 | 8750 | 0.5270 | 0.9045 |
| 0.0 | 25.01 | 9100 | 0.5410 | 0.8992 |
| 0.0266 | 26.01 | 9450 | 0.6122 | 0.8899 |