Instructions to use Francesco/resnet101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Francesco/resnet101 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Francesco/resnet101") 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("Francesco/resnet101") model = AutoModelForImageClassification.from_pretrained("Francesco/resnet101") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 716d34b572a635bb9fec0f676637ca0daba915381d52b9c26d5626b9bf2fed07
- Size of remote file:
- 179 MB
- SHA256:
- aeca56057cc6c55b9ba7a29111683868ee9c6744d32bac8a0afd2de8557e457e
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