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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: 0.3441 Accuracy: 0.9127
Model source
Source description
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 sourceVerified Sep 18
Model artifacts
1 artifactSource excerpts
2 excerptsMore information needed
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0051 | 0.01 | 687 | 0.3061 | 0.9202 |
| 0.0083 | 1.01 | 1374 | 0.5940 | 0.8527 |
| 0.0201 | 2.01 | 2061 | 0.6432 | 0.8296 |
| 1.182 | 3.01 | 2748 | 1.0621 | 0.7523 |
| 0.3175 | 4.01 | 3435 | 1.0938 | 0.7926 |
--- 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-0325_final_randomclip 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-0325_final_randomclip 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: 0.3441 - Accuracy: 0.9127 ## 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0051 | 0.01 | 687 | 0.3061 | 0.9202 | | 0.0083 | 1.01 | 1374 | 0.5940 | 0.8527 | | 0.0201 | 2.01 | 2061 | 0.6432 | 0.8296 | | 1.182 | 3.01 | 2748 | 1.0621 | 0.7523 | | 0.3175 | 4.01 | 3435 | 1.0938 | 0.7926 | | 0.0086 | 5.01 | 4122 | 0.4178 | 0.8815 | | 0.0294 | 6.01 | 4809 | 0.6779 | 0.8239 | | 0.105 | 7.01 | 5496 | 1.0207 | 0.8041 | | 0.005 | 8.01 | 6183 | 0.5790 | 0.8494 | | 0.3475 | 9.01 | 6870 | 1.4327 | 0.7407 | | 0.5972 | 10.01 | 7557 | 0.7518 | 0.8543 | | 0.5834 | 11.01 | 8244 | 1.1018 | 0.7877 | | 0.0075 | 12.01 | 8931 | 0.7481 | 0.8420 | | 0.5742 | 13.01 | 9618 | 0.9245 | 0.8058 | | 0.0513 | 14.01 | 10305 | 0.8096 | 0.8181 | | 0.2228 | 15.01 | 10992 | 0.8514 | 0.7630 | ### Framework versions - Transformers 4.48.0 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
Source context: 5 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo d2o2ji/videomae-base-finetuned-kinetics-0325_final_randomclip
| 0.0086 | 5.01 | 4122 | 0.4178 | 0.8815 |
| 0.0294 | 6.01 | 4809 | 0.6779 | 0.8239 |
| 0.105 | 7.01 | 5496 | 1.0207 | 0.8041 |
| 0.005 | 8.01 | 6183 | 0.5790 | 0.8494 |
| 0.3475 | 9.01 | 6870 | 1.4327 | 0.7407 |
| 0.5972 | 10.01 | 7557 | 0.7518 | 0.8543 |
| 0.5834 | 11.01 | 8244 | 1.1018 | 0.7877 |
| 0.0075 | 12.01 | 8931 | 0.7481 | 0.8420 |
| 0.5742 | 13.01 | 9618 | 0.9245 | 0.8058 |
| 0.0513 | 14.01 | 10305 | 0.8096 | 0.8181 |
| 0.2228 | 15.01 | 10992 | 0.8514 | 0.7630 |
