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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Woleek/ResNet50AffectiveFeatureExtractor", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 226 Bytes
fc8b3f4 | 1 2 3 4 5 6 7 8 9 10 | from transformers import PretrainedConfig
class ResnetConfig(PretrainedConfig):
model_type = "ResNet50AffectiveFeatureExtractor"
def __init__(
self,
**kwargs,
):
super().__init__(**kwargs) |