Instructions to use haiderAI/resnet50-rice-disease-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haiderAI/resnet50-rice-disease-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="haiderAI/resnet50-rice-disease-detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("haiderAI/resnet50-rice-disease-detection") model = AutoModelForImageClassification.from_pretrained("haiderAI/resnet50-rice-disease-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from haiderAI/resnet50-rice-disease-detection: direct link, hf CLI and curl.
- Browser
- Download file 94.4 MB
-
https://huggingface.co/haiderAI/resnet50-rice-disease-detection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://haiderAI/resnet50-rice-disease-detection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/haiderAI/resnet50-rice-disease-detection/resolve/main/pytorch_model.bin
94.4 MB
- Xet hash:
- 59d495532612597bcf4c124b876d2731cdd8993bd281a8247e489b5cd7318d20
- Size of remote file:
- 94.4 MB
- SHA256:
- 032937cb357e25b90c11a8bc01a8a5e9b29403f7f33c3a2776570a0430b18d98
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