Image Classification
Transformers.js
ONNX
PyTorch
Transformers
vit
vision
facial-expression-recognition
emotion-detection
Instructions to use onnx-community/face-emotion-detection-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use onnx-community/face-emotion-detection-ONNX with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-classification', 'onnx-community/face-emotion-detection-ONNX'); - Transformers
How to use onnx-community/face-emotion-detection-ONNX with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="onnx-community/face-emotion-detection-ONNX") 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("onnx-community/face-emotion-detection-ONNX") model = AutoModelForImageClassification.from_pretrained("onnx-community/face-emotion-detection-ONNX", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from onnx-community/face-emotion-detection-ONNX: direct link, hf CLI and curl.
- Browser
- Download file 351 Bytes
-
https://huggingface.co/onnx-community/face-emotion-detection-ONNX/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://onnx-community/face-emotion-detection-ONNX/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/onnx-community/face-emotion-detection-ONNX/resolve/main/preprocessor_config.json
351 Bytes
| { | |
| "do_convert_rgb": null, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "ViTImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| } | |
| } | |