alchemydetect 0.3.0

Desktop GUI application for training and running inference with Detectron2 models

AlchemyDetect

A desktop GUI application for training and running inference with Detectron2 models.

Features

• Train object detection and instance segmentation models with a visual interface
• Live monitoring — real-time loss plot and training logs
• Inference on single images or entire folders with result visualization
• Model management — save and load trained weights for later use

Supported Models

Model	Task
Faster R-CNN (R50-FPN, R101-FPN)	Object Detection
RetinaNet (R50-FPN, R101-FPN)	Object Detection
Mask R-CNN (R50-FPN, R101-FPN)	Instance Segmentation

Quick Start

# Install dependencies (see INSTALL.md for detailed setup)
pip install -r requirements.txt

# Run the application
python main.py

Dataset Format

AlchemyDetect uses COCO JSON format for training datasets. You need:

• A directory containing your training images
• A COCO-format JSON annotation file

Usage

Training

1. Open the Train tab
2. Select your training images directory and COCO JSON annotation file
3. Choose a model architecture from the dropdown
4. Set hyperparameters (learning rate, iterations, batch size)
5. Choose an output directory
6. Click Start Training
7. Monitor progress via the log viewer and loss plot

Inference

1. Open the Inference tab
2. Click Load Model and select a trained .pth file (config.yaml will be auto-detected if in the same directory)
3. Adjust the confidence threshold
4. Click Run on Image or Run on Folder
5. Browse results using the navigation buttons

Tech Stack

• Python 3.10 or 3.11
• PyQt6 — Desktop GUI
• Detectron2 — Object detection / instance segmentation
• PyTorch — Deep learning backend
• pyqtgraph — Real-time loss plotting

License

MIT