metadata
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:
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()}")