Instructions to use chandra1976/vit-facial-expression-fatigue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chandra1976/vit-facial-expression-fatigue with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="chandra1976/vit-facial-expression-fatigue") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("chandra1976/vit-facial-expression-fatigue") model = AutoModelForImageClassification.from_pretrained("chandra1976/vit-facial-expression-fatigue", device_map="auto") - Notebooks
- Google Colab
- Kaggle
vit-facial-expression-fatigue
This model is a fine-tuned version of mo-thecreator/vit-Facial-Expression-Recognition on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3637
- Accuracy: 0.9318
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2329 | 1.0 | 110 | 0.2056 | 0.9136 |
| 0.0923 | 2.0 | 220 | 0.1680 | 0.9409 |
| 0.0233 | 3.0 | 330 | 0.2320 | 0.9364 |
| 0.0084 | 4.0 | 440 | 0.2685 | 0.9409 |
| 0.002 | 5.0 | 550 | 0.3637 | 0.9318 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.10.0
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for chandra1976/vit-facial-expression-fatigue
Evaluation results
- Accuracy on imagefolderself-reported0.932