Image Classification
Transformers
PyTorch
resnet
resnet50
agriculture
anomaly-detection
wheat
plant-disease
Instructions to use jays009/Resnet3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jays009/Resnet3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jays009/Resnet3") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jays009/Resnet3", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4345a7dbd0310b60d12c6a637b17df5547810fe13fc4ef12c0c9e6880390662a
- Size of remote file:
- 281 MB
- SHA256:
- 3a302a3f6aaa0756ef0eded4478fc83436a42498a6a77bb90c1659347e79a068
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