Instructions to use Woleek/ResNet50AffectiveFeatureExtractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Woleek/ResNet50AffectiveFeatureExtractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Woleek/ResNet50AffectiveFeatureExtractor", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Woleek/ResNet50AffectiveFeatureExtractor", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Woleek/ResNet50AffectiveFeatureExtractor: direct link, hf CLI and curl.
- Browser
- Download file 307 Bytes
-
https://huggingface.co/Woleek/ResNet50AffectiveFeatureExtractor/resolve/main/config.json
- Command line
-
hf download hf://Woleek/ResNet50AffectiveFeatureExtractor/config.json
-
curl -L -o config.json https://huggingface.co/Woleek/ResNet50AffectiveFeatureExtractor/resolve/main/config.json
307 Bytes
| { | |
| "architectures": [ | |
| "ResNet50AffectiveFeatureExtractor" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "config.ResnetConfig", | |
| "AutoModel": "model.ResNet50AffectiveFeatureExtractor" | |
| }, | |
| "model_type": "ResNet50AffectiveFeatureExtractor", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.47.1" | |
| } | |