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 training_args.bin from haiderAI/resnet50-rice-disease-detection: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/haiderAI/resnet50-rice-disease-detection/resolve/main/training_args.bin
- Command line
-
hf download hf://haiderAI/resnet50-rice-disease-detection/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/haiderAI/resnet50-rice-disease-detection/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- afba4155d19da2572f25ec72786be31072fdc28ea10231ac8f850dd57831c722
- Size of remote file:
- 3.96 kB
- SHA256:
- fdd110bd53e3614f9ab9e6f4ce3a7360327eab3d5ffecad2807c3953edd39394
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