VideoMAE Kinetics Fold 8 BdSLW60 SKF
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: 0.0138 Accuracy: 0.9975
Model source
- Kind
- Model
- File size
- 329 MB
- Base model
- MCG-NJU/videomae-base-finetuned-kinetics
- Version
- vRevision e9cf9c1
- Updated
- Oct 30, 2026
- Downloads
- 4
- Source
- Hugging Face
Source description
VideoMAE_Kinetics_fold__8__BdSLW60_SKF
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: 0.0138
- Accuracy: 0.9975
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 |
|---|---|---|---|---|
| 8.2629 | 0.1 | 903 | 0.2782 | 0.9589 |
| 0.3726 | 1.1001 | 1807 | 0.0554 | 0.9863 |
| 0.0963 | 2.1 | 2710 | 0.0455 | 0.9913 |
| 0.0519 | 3.1001 | 3614 | 0.0183 | 0.9950 |
| 0.03 | 4.1 | 4517 | 0.0147 | 0.9963 |
| 0.0322 | 5.1001 | 5421 | 0.0086 | 0.9963 |
| 0.0127 | 6.1 | 6324 | 0.0070 | 0.9975 |
| 0.01 | 7.1001 | 7228 | 0.0158 | 0.9938 |
| 0.0021 | 8.1 | 8131 | 0.0356 | 0.9950 |
| 0.001 | 9.0995 | 9030 | 0.0138 | 0.9975 |
Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.1
Sources
1 source
Sources
1 sourceVerified Oct 30
Download files
Repository files
File inventory only; completeness is unverified.
Model artifacts
2 artifacts
Model artifacts
2 artifactscheckpoint/model.safetensors
safetensors · FP32 (tensor header) · 329 MB · SHA-256 b9c9db4094d1…b641 · Hugging Face
Download filemodel.safetensors
safetensors · FP32 (tensor header) · 329 MB · SHA-256 b9c9db4094d1…b641 · Hugging Face
Download fileSource excerpts
3 excerpts
Source excerpts
3 excerpts--- 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_Kinetics_fold__8__BdSLW60_SKF 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_Kinetics_fold__8__BdSLW60_SKF 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: 0.0138 - Accuracy: 0.9975 ## 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 | |:-------------:|:------:|:----:|:---------------:|:--------:| | 8.2629 | 0.1 | 903 | 0.2782 | 0.9589 | | 0.3726 | 1.1001 | 1807 | 0.0554 | 0.9863 | | 0.0963 | 2.1 | 2710 | 0.0455 | 0.9913 | | 0.0519 | 3.1001 | 3614 | 0.0183 | 0.9950 | | 0.03 | 4.1 | 4517 | 0.0147 | 0.9963 | | 0.0322 | 5.1001 | 5421 | 0.0086 | 0.9963 | | 0.0127 | 6.1 | 6324 | 0.0070 | 0.9975 | | 0.01 | 7.1001 | 7228 | 0.0158 | 0.9938 | | 0.0021 | 8.1 | 8131 | 0.0356 | 0.9950 | | 0.001 | 9.0995 | 9030 | 0.0138 | 0.9975 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.1
Source context: 4 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/VideoMAE_Kinetics_fold__8__BdSLW60_SKF
Source context: 25 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/VideoMAE_Kinetics_fold__8__BdSLW60_SKF
