metadata

model_name: Wheat Anomaly Detection Model
tags:
  - pytorch
  - resnet
  - agriculture
  - anomaly-detection
  - image-classification
  - wheat-disease-detection
  - pest-detection
  - agricultural-ai
license: apache-2.0
library_name: pytorch
datasets:
  - wheat-dataset
model_type: resnet50
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

Model Overview

This model is trained to detect anomalies in wheat crops, such as pest infections (e.g., Fall Armyworm), diseases, or nutrient deficiencies. The model is based on the ResNet50 architecture and was fine-tuned on a dataset of wheat images.

Model Details

Model Architecture: ResNet50
Number of Classes: 2 (Fall Armyworm, Healthy Wheat)
Input Shape: 224x224 pixels, 3 channels (RGB)
Training Framework: PyTorch
Optimizer: Adam
Learning Rate: 0.001
Epochs: 20
Batch Size: 32

Training

The model was fine-tuned using a balanced dataset with images of healthy wheat and wheat infected by fall armyworms. The training involved transferring knowledge from a pretrained ResNet50 model and adjusting the final classification layer for the binary classification task.

Dataset

The model was trained on a dataset hosted on Hugging Face. You can access it here:

Dataset: your_huggingface_username/your_dataset_name

How to Use

To load and use this model in PyTorch, follow the steps below:

1. Load the Model

import torch
import timm

# Load the pre-trained model (fine-tuned ResNet50 for wheat anomaly detection)
model = timm.create_model("resnet50", pretrained=False, num_classes=2)
model.load_state_dict(torch.load("path_to_saved_model.pth"))
model.eval()