YoloLabel

Summary

YoloLabel is a free desktop GUI for marking object bounding boxes in images used to train YOLO neural networks. It loads JPG or PNG images from a directory and creates boxes with a two-click method. Annotation tools include moving, resizing, copying, pasting, undoing, and redoing labels, which can be exported in YOLO TXT format. For assisted labeling, the app can run local inference with Ultralytics detection models exported to ONNX, including YOLOv5, YOLOv8, YOLO11, YOLO12, and YOLOv26. A loaded model can label the current image or batch-process a dataset. YoloLabel also connects to YoloLabel AI for cloud open-vocabulary detection using an API key and optional prompt; cloud batch requests handle up to 20 images at a time. Prebuilt downloads are listed for Windows x64, Linux x64, and macOS on Apple Silicon, and the desktop repository uses the MIT License. Building from source requires Qt 6, while ONNX Runtime is optional unless local auto-labeling is needed. The app warns that moving the horizontal image slider does not automatically save the last processed image.

Who it is for

YoloLabel suits people preparing YOLO training data who need manual boxes or local ONNX-assisted labeling. Its cloud option may suit users who want prompt-based detection and can work within the service's stated limits.

Compared on AI image annotation tools

Free plan

Yesgithub.com

Annotation types

bounding boxesgithub.com

AI-assisted labeling

Yesgithub.com

Review workflow

Yesgithub.com

Export formats

YOLO TXTgithub.com

Deployment

self-hostedgithub.com

Facts

Purpose

YoloLabel is a GUI for marking object bounding boxes in images to train YOLO neural networks.github.com · 30 Sept 2026

Annotation

It supports manual bounding box labeling and uses a two left-click method to create boxes.github.com · 30 Sept 2026

Image formats

The README says to load .jpg or .png images from a directory.github.com · 30 Sept 2026

Local auto-labeling

It can run local inference with Ultralytics detection models exported to ONNX, including YOLOv5, YOLOv8, YOLO11, YOLO12, and YOLOv26.github.com · 30 Sept 2026

Batch labeling

With a loaded ONNX model, users can auto-label the current image or batch-process all images in the dataset.github.com · 30 Sept 2026

Cloud integration

YoloLabel integrates with yololabel.com for cloud open-vocabulary object detection, using an API key and optional detection prompt.github.com · 30 Sept 2026

Cloud batch limit

Cloud Auto Label All submits images in batches of up to 20 per request.github.com · 30 Sept 2026

Image tools

The app includes real-time contrast adjustment and a usage timer that runs while its window is focused.github.com · 30 Sept 2026

Download platforms

The README lists prebuilt downloads for Windows x64, Linux x64, and macOS on Apple Silicon.github.com · 30 Sept 2026

Build requirement

Building from source with auto-label support requires ONNX Runtime; without it, the app works without that feature.github.com · 30 Sept 2026

Usage caveat

The README warns that moving the horizontal image slider does not automatically save the last processed image.github.com · 30 Sept 2026

Maker

The maker’s GitHub profile identifies developer0hye as Yonghye Kwon.github.com · 30 Sept 2026

Manual annotation

It uses a two-click method to create boxes and includes tools to move, resize, copy, paste, undo, and redo annotations.github.com · 30 Sept 2026

Supported models

The README lists YOLOv5, YOLOv8, YOLO11, YOLO12, YOLOv26, and end-to-end ONNX models as supported for auto-labeling.github.com · 30 Sept 2026

Cloud API

YoloLabel AI provides a REST API that accepts images and prompts and returns detections and YOLO-format labels.yololabel.com · 30 Sept 2026

Downloads

Prebuilt desktop downloads are listed for Windows x64, Linux x64, and macOS Apple Silicon.github.com · 30 Sept 2026

Source build

The project says it can be built from source with Qt 6; ONNX Runtime is optional for builds that need local auto-labeling.github.com · 30 Sept 2026

License

The desktop repository is licensed under the MIT License, which permits use, modification, distribution, and sale subject to its stated conditions.github.com · 30 Sept 2026

Cloud image handling

The cloud service privacy policy says uploaded images are processed in memory, discarded after inference, and not used to train models.yololabel.com · 30 Sept 2026

Cloud security

The cloud privacy policy says it uses HTTPS, bcrypt password hashing, and short-lived JWTs with refresh token rotation.yololabel.com · 30 Sept 2026

Cloud data retention

The cloud service says it retains job metadata for 90 days and account and usage records while an account is active.yololabel.com · 30 Sept 2026

Cloud limits

The cloud service terms state that the free tier includes 100 images per month with no SLA, unused quota does not roll over, and over-limit requests return HTTP 402.yololabel.com · 30 Sept 2026

Support

The cloud service lists [email protected] as its contact email for questions about its terms and privacy policy.yololabel.com · 30 Sept 2026

Where it ranks on Everything Xiaomi

• Best AI Image Annotation Tools in 2026#8 of 24

Sources

• github.com/developer0hye/Yolo_Label· checked 30 Sept 2026
• github.com/developer0hye· checked 30 Sept 2026
• yololabel.com· checked 30 Sept 2026
• github.com/developer0hye/Yolo_Label/blob/master/LI· checked 30 Sept 2026
• yololabel.com/privacy· checked 30 Sept 2026
• yololabel.com/terms· checked 30 Sept 2026