VideoMAE Base Wlasl 100 20ep FailureCase
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: 1.3033 Accuracy: 0.5312
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 329 MB
- Modelo base
- MCG-NJU/videomae-base-finetuned-kinetics
- Versión
- vRevision fb69538
- Actualizado
- 24 oct 2026
- Descargas
- 8
- Fuente
- Hugging Face
Descripción de la fuente
videoMAE_base_wlasl_100_20ep_failureCase
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: 1.3033
- Accuracy: 0.5312
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: 85
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 9.557 | 0.2 | 17 | 2.2345 | 0.1875 |
| 8.0214 | 1.2059 | 35 | 1.9148 | 0.3125 |
| 6.1022 | 2.2 | 52 | 1.5669 | 0.5625 |
| 3.7933 | 3.2059 | 70 | 1.3570 | 0.5625 |
| 2.8819 | 4.1765 | 85 | 1.3033 | 0.5312 |
Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.1
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 24 oct
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- 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_wlasl_100_20ep_failureCase 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_wlasl_100_20ep_failureCase 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: 1.3033 - Accuracy: 0.5312 ## 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: 85 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 9.557 | 0.2 | 17 | 2.2345 | 0.1875 | | 8.0214 | 1.2059 | 35 | 1.9148 | 0.3125 | | 6.1022 | 2.2 | 52 | 1.5669 | 0.5625 | | 3.7933 | 3.2059 | 70 | 1.3570 | 0.5625 | | 2.8819 | 4.1765 | 85 | 1.3033 | 0.5312 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.1
Source context: 8 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/videoMAE_base_wlasl_100_20ep_failureCase
Source context: 2 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo Shawon16/videoMAE_base_wlasl_100_20ep_failureCase
