Instructions to use leftthomas/resnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leftthomas/resnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="leftthomas/resnet50", trust_remote_code=True) 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("leftthomas/resnet50", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("leftthomas/resnet50", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 87be415dd25bc476d1354d58d2a169a7e1e22244687781a868e3d47f73ebd365
- Size of remote file:
- 103 MB
- SHA256:
- e9a62d6dd33235132b42cd71e211dd9bcdd013a80d2ba9a0bdcd32bffcd07447
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