Instructions to use Francesco/resnet34 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Francesco/resnet34 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Francesco/resnet34") 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/resnet34") model = AutoModelForImageClassification.from_pretrained("Francesco/resnet34") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8914d6fec0a77b469e53e00286a228d0251a87a7f67b3d2d9fdcd97924700c59
- Size of remote file:
- 87.3 MB
- SHA256:
- 98716c6143dbf037a727178bbfcd26aee604531ad06e5a60815f664d7e19add6
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