Instructions to use jays009/Restnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jays009/Restnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jays009/Restnet50") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jays009/Restnet50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- resnet
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- agriculture
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- anomaly-detection
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- image-classification
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license: apache-2.0
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library_name: pytorch
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- your_huggingface_username/your_dataset_name # Replace with your actual dataset name
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pipeline_tag: image-classification # This explicitly defines the task pipeline
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---
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# Wheat Anomaly Detection Model
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- resnet
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- agriculture
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- anomaly-detection
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- image-classification
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license: apache-2.0
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library_name: pytorch
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pipeline_tag: image-classification
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---
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# Wheat Anomaly Detection Model
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