Instructions to use sgugger/custom-resnet50d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgugger/custom-resnet50d with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sgugger/custom-resnet50d", 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("sgugger/custom-resnet50d", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("sgugger/custom-resnet50d", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e0f320c83479f16504686486ecb21b400e8fe7a84aef8a228bebe2d11a363ea
- Size of remote file:
- 103 MB
- SHA256:
- 25a41ac7007f542318ceaadf94bf3d75c55d1813219a634b35d6722f10fc91c2
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