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.py from Woleek/ResNet50AffectiveFeatureExtractor: direct link, hf CLI and curl.
- Browser
- Download file 226 Bytes
-
https://huggingface.co/Woleek/ResNet50AffectiveFeatureExtractor/resolve/main/config.py
- Command line
-
hf download hf://Woleek/ResNet50AffectiveFeatureExtractor/config.py
-
curl -L -o config.py https://huggingface.co/Woleek/ResNet50AffectiveFeatureExtractor/resolve/main/config.py
226 Bytes
| from transformers import PretrainedConfig | |
| class ResnetConfig(PretrainedConfig): | |
| model_type = "ResNet50AffectiveFeatureExtractor" | |
| def __init__( | |
| self, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) |