https://github.com/who-else-but-arjun/graph_se_weed_detect_krenge
This work explores the power of semi-supervised learning (SSL), which combines labeled and unlabeled data to im- prove weed detection accuracy. This study presents a novel pipeline integrating YOLO-based object detection, pseudo-labeling, and Graph Neural Networks (GNNs) for transductive learning.
https://github.com/who-else-but-arjun/graph_se_weed_detect_krenge
graph-neural-networks knn object-detection semi-supervised-learning weed-detection yolo11
Last synced: about 2 months ago
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This work explores the power of semi-supervised learning (SSL), which combines labeled and unlabeled data to im- prove weed detection accuracy. This study presents a novel pipeline integrating YOLO-based object detection, pseudo-labeling, and Graph Neural Networks (GNNs) for transductive learning.
- Host: GitHub
- URL: https://github.com/who-else-but-arjun/graph_se_weed_detect_krenge
- Owner: who-else-but-arjun
- Created: 2025-02-03T20:08:59.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-05T14:31:40.000Z (4 months ago)
- Last Synced: 2025-02-13T13:28:15.860Z (3 months ago)
- Topics: graph-neural-networks, knn, object-detection, semi-supervised-learning, weed-detection, yolo11
- Language: Jupyter Notebook
- Homepage: https://drive.google.com/file/d/1qe5sccHGi8ZNHXP75NCLnfC4XMNwUHSc/view?usp=sharing
- Size: 170 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Semi-Supervised Object Detection with Graph Neural Networks
This repository implements a semi-supervised learning approach for weed detection using YOLO and Graph Neural Networks. The system leverages both labeled and unlabeled data to improve detection performance through pseudo-labeling. The dataset is available [here](https://drive.google.com/drive/folders/1cGY1QHqNFiB2qkeL_rS0KtPDv54qk-Ks?usp=sharing). The detailed project report can be found [here](https://drive.google.com/file/d/1qe5sccHGi8ZNHXP75NCLnfC4XMNwUHSc/view?usp=sharing).
## Project Structure
```
├── src/
│ ├── __init__.py
│ ├── feature_extractor.py # Feature extraction from images
│ ├── graph_constructor.py # KNN graph construction
│ ├── gnn_model.py # GNN model definition
│ ├── trainer.py # Training logic
│ └── utils.py # Utility functions
├── main.py # Main training script
├── requirements.txt # Dependencies
└── README.md # This file
```
The `FINAL.ipynb` contains the training outputs performed on Kaggle. The notebook contains all the code with training as well as inference with proper file paths. This `notebook.ipynb` can be used for inference on the test data using the `yaml` files. The trained model file for the final inference is in the `train_final` folder with `best.pt` name. `EXTRA` folder contains the trained GNN model as `WEED.pt`.
## Installation
```bash
pip install -r requirements.txt
```
## Required Dependencies
- PyTorch
- PyTorch Geometric
- Ultralytics (YOLOv8)
- scikit-learn
- numpy
- Pillow
## Usage
1. Prepare your dataset in the following structure:
```
data/
├── labeled/
│ ├── images/
│ │ └── *.jpg
│ └── labels/
│ └── *.txt
└── unlabeled/
└── *.jpg
```
2. Create YAML files for your dataset configuration:
- `dataset.yaml`: Initial training configuration
- `dataset_updated.yaml`: Configuration for final training
- `test.yaml`: Test dataset configuration
Edit the path in yaml files according to your setup. Try with the complete path in case the relative path is not read.
3. Run the training:
```bash
python main.py \
--labeled_dir data/labeled \
--unlabeled_dir data/unlabeled \
--dataset_yaml path/to/dataset.yaml \
--dataset_updated_yaml path/to/dataset_updated.yaml \
--test_yaml path/to/test.yaml \
--output_dir output \
--yolo_model yolo11m.pt
```
## Command Line Arguments
- `--labeled_dir`: Directory containing labeled images and annotations
- `--unlabeled_dir`: Directory containing unlabeled images
- `--dataset_yaml`: Path to initial dataset YAML file
- `--dataset_updated_yaml`: Path to updated dataset YAML file
- `--test_yaml`: Path to test dataset YAML file
- `--output_dir`: Directory to save outputs
- `--yolo_model`: Path to pretrained YOLO model
- `--epochs`: Number of epochs (default: 500)
- `--batch_size`: Batch size (default: 32)
- `--learning_rate`: Initial learning rate (default: 0.0003)
- `--gnn_hidden_dim`: Hidden dimension for GNN (default: 128)
- `--confidence_threshold`: Threshold for pseudo-labeling (default: 0.9)
or
Run the initial cells in `notebook.ipynb` until the imports. After that run the last cells. Check that the path of the model passed to the YOLO is present at the path.
`model = YOLO("train_final/best.pt")
metrics = model.val(data="yaml-files/test.yaml", device="cuda")`
set the device to cpu in case running on cpu
`#test.yaml
path:
train: labeled/images
val: test/images
test: test/images
nc: 2 # Number of classes
names: ["weed", "crop"]
`
## Method Overview
1. **Initial YOLO Training**: Train YOLO model on labeled data
2. **Feature Extraction**: Extract features from both labeled and unlabeled images
3. **Graph Construction**: Build KNN graph from image features
4. **GNN Training**: Train GNN for pseudo-label generation
5. **Pseudo-labeling**: Generate labels for unlabeled images
6. **Final Training**: Retrain YOLO with both labeled and pseudo-labeled data
## Performance Metrics
The system evaluates performance using:
- Mean Precision
- Mean Recall
- mAP@50
- mAP@50-95
- F1-Score
- Final Score (0.5 * F1-Score + 0.5 * mAP@50-95)