Instructions to use trpakov/vit-face-expression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trpakov/vit-face-expression with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="trpakov/vit-face-expression") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("trpakov/vit-face-expression") model = AutoModelForImageClassification.from_pretrained("trpakov/vit-face-expression", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
-
Improve model card: add usage example, fix preprocessing details, expand limitations
Verified signatureAdd an extended description including: - Working code example using the transformers pipeline - Updated preprocessing details with specific values (224x224, ImageNet normalization) - Expanded limitations section with concrete details on dataset bias, class imbalance, skin tone bias, and input requirements7a73a2b
-
Update README.md
Verified signatureef0bc6f
-
Adding ONNX file of this model (#2)
- Adding ONNX file of this model (edee1fd1327806713c67949575a16fec92990917) Co-authored-by: Auyez Khassenov <auyezkhassenov@users.noreply.huggingface.co>78ed8d3 -
Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (2428fbf94162aabf4304c7e7c3df261f962d5697) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>c09980e -
Create README.md
a2346f6
-
Upload feature extractor
24d2953 -
Upload ViTForImageClassification
561fede -
Upload ViTForImageClassification
4f96cb7 -
initial commit
5eac4c0