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---
model_name: Wheat Anomaly Detection Model
tags:
- pytorch
- resnet
- agriculture
- anomaly-detection
license: apache-2.0
library_name: transformers
datasets:
- wheat-disease-dataset
model_type: resnet
preprocessing:
- resize: 256
- center_crop: 224
- normalize: [0.485, 0.456, 0.406]
- normalize_std: [0.229, 0.224, 0.225]
framework: pytorch
task: image-classification
pipeline_tag: image-classification
---
# Wheat Anomaly Detection Model
This model is a PyTorch-based ResNet model trained to detect anomalies in wheat crops, such as diseases, pests, and nutrient deficiencies.
## How to Load the Model
To load the trained model, use the following code:
```python
from transformers import AutoModelForImageClassification
import torch
# Load the pre-trained model
model = AutoModelForImageClassification.from_pretrained('your_huggingface_username/your_model_name')
# Put the model in evaluation mode
model.eval()
# Example of making a prediction
image_path = "path_to_your_image.jpg" # Replace with your image path
image = Image.open(image_path)
inputs = transform(image).unsqueeze(0) # Apply the necessary transformations to the image
inputs = inputs.to(device)
# Make a prediction
with torch.no_grad():
outputs = model(inputs)
predicted_class = torch.argmax(outputs.logits, dim=1)
print(f"Predicted Class: {predicted_class.item()}") |