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--- |
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model_name: "Wheat Anomaly Detection Model" |
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tags: |
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- pytorch |
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- resnet |
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- agriculture |
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- anomaly-detection |
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license: apache-2.0 |
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library_name: pytorch |
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datasets: |
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- your_huggingface_username/your_dataset_name |
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--- |
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# Wheat Anomaly Detection Model |
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This model is a PyTorch-based ResNet model trained to detect anomalies in wheat crops, such as diseases, pests, and nutrient deficiencies. |
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## How to Load the Model |
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To load the trained model, use the following code: |
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```python |
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from transformers import AutoModelForImageClassification |
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import torch |
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# Load the pre-trained model |
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model = AutoModelForImageClassification.from_pretrained('your_huggingface_username/your_model_name') |
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# Put the model in evaluation mode |
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model.eval() |
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# Example of making a prediction |
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image_path = "path_to_your_image.jpg" # Replace with your image path |
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image = Image.open(image_path) |
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inputs = transform(image).unsqueeze(0) # Apply the necessary transformations to the image |
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inputs = inputs.to(device) |
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# Make a prediction |
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with torch.no_grad(): |
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outputs = model(inputs) |
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predicted_class = torch.argmax(outputs.logits, dim=1) |
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print(f"Predicted Class: {predicted_class.item()}") |
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