YOLO Studio

YOLO Studio is a desktop developer tool for training, organizing, and remotely deploying YOLO computer vision models.

Project Overview

This project provides:

• Local dataset management and dataset-building workflows
• Configurable YOLO training with live metrics and logs
• Discovery integrations for Roboflow Universe, Hugging Face Hub, Kaggle, and Open Images
• Remote deployment and testing against Jetson/Raspberry Pi edge devices
• Real-time camera inference with session capture
• Offline evaluation with analytics dashboards
• Cross-run analytics and experiment comparison
• Project system for organizing assets under named roots

Setup

1. Create and activate a Python 3.10+ virtual environment.
2. Install dependencies:

pip install -r requirements.txt

3. Launch the app:

python main.py

Architecture

+-------------------------- YOLO Studio (PyQt6) ---------------------------+
|                                                                          |
|  +---------------- UI Layer ----------------+    +------ Widgets -------+ |
|  | train | dataset | discover | remote      |    | log | chart | cards | |
|  | camera | evaluate | analytics            |    +----------------------+ |
|  +------------------------------------------+                           |
|                 |                                                    |
|                 v                                                    |
|  +------------------ Core Layers ----------------------------+         |
|  | models | services | workers | projects                   |         |
|  +----------------------------------------------------------+         |
|                 |                                                    |
|                 v                                                    |
|  +------------------------- Integrations ---------------------------+  |
|  | ultralytics | roboflow | huggingface_hub | websocket devices    |  |
|  +------------------------------------------------------------------+  |
+--------------------------------------------------------------------------+

Edge Device Agent (Jetson/RPi): edge/jetson_agent.py

Code Layout

• core/models/: SQLAlchemy entities and DB session/bootstrap logic
• core/services/: dataset and remote-device service classes
• core/workers/: QThread background worker classes
• ui/windows/: top-level window classes
• ui/styles/: theme and styling modules
• ui/tabs/, ui/widgets/: feature tabs and reusable UI components
• Compatibility shims remain at legacy module paths (core/database.py, etc.) for safer import migration.

Usage Guide

Training Tab

• Configure model architecture, dataset, and hyperparameters
• Start/stop training jobs with live metrics and logs
• Save successful runs and manage trained weights

Dataset Tab

• Browse datasets stored in SQLite
• Build datasets from local images and generate YOLO metadata
• Review and export saved models Note: Project scope filters the dataset library and saved models.

Discover Tab

• Search and download datasets from Roboflow Universe
• Search and download models/datasets from Hugging Face Hub
• Search Kaggle datasets/competitions and download datasets
• Download Open Images subsets with class filters Note: Imported assets can be attached to the active project.

Remote Devices Tab

• Register edge devices
• Deploy model weights remotely
• Run remote inference tests and store result metrics

Camera Tab

• Run real-time webcam inference with overlays
• Record frames, save snapshots, export detections CSV
• Session metadata stored in the database

Evaluate Tab

• Offline evaluation for datasets or image folders
• Confusion matrix, PR curves, and confidence curves
• Export reports as PDF + JSON

Analytics Tab

• Cross-run history, loss overlays, and per-class heatmaps
• Compare runs and export dashboard ZIP

Project System

• Create/open/duplicate/delete projects
• Store project config in project.yaml
• Project selector filters all tabs

Edge Agent Setup

1. Copy edge/jetson_agent.py and edge/agent_config.yaml to the device.
2. Install Python dependencies on the device.
3. Configure agent_config.yaml.
4. Start the agent and verify connectivity from YOLO Studio.

Troubleshooting

• Ensure Python version is 3.10+.
• Confirm config.json contains valid API tokens when using external services.
• Verify CUDA/device drivers for GPU-enabled edge inference.
• Check application and edge agent logs for runtime errors.
• On Linux desktops without working Wayland or XCB dependencies (e.g. missing libxcb-cursor0), YOLO Studio falls back to Qt's minimal platform. You can override with QT_QPA_PLATFORM if you want to force a specific backend.

Current Status

The application scaffold and core feature modules are implemented, including:

• SQLAlchemy-backed metadata models and persistence helpers
• Training, dataset, discover, and remote-device GUI tabs
• QThread workers for long-running operations
• WebSocket remote manager and edge agent protocol implementation