YoloLabel

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What YoloLabel is

YoloLabel is a free desktop GUI for drawing object bounding boxes on images used to train YOLO neural networks. It loads JPG or PNG files from a directory and creates boxes with a two-click method; users can also move, resize, copy, paste, undo, and redo annotations, then export them as YOLO TXT labels. For assisted labeling, it can run local inference with supported Ultralytics detection models exported to ONNX, including YOLOv5, YOLOv8, YOLO11, YOLO12, and YOLOv26. A loaded model can label the current image or process a dataset in batches. The app also connects to YoloLabel AI for cloud open-vocabulary detection using an API key and optional prompt; cloud batches contain up to 20 images per request. Prebuilt desktop downloads are listed for Windows x64, Linux x64, and macOS on Apple Silicon, and the project uses the MIT License. Building from source requires Qt 6; ONNX Runtime is optional unless local auto-labeling is wanted. The cloud service's free tier allows 100 images per month, has no SLA, and returns HTTP 402 for over-limit requests. The app warns that moving the horizontal image slider does not automatically save the last processed image.

Who it suits

YoloLabel suits people preparing image annotations for YOLO training who want manual box editing or assisted labeling. It offers local ONNX inference as well as an optional cloud detection service.

What it does well

Free desktop application with MIT-licensed repository
Manual box editing includes undo and redo
Can batch-label with a local ONNX model
Downloads for Windows, Linux, and Apple Silicon

What to know first

Cloud free tier allows 100 images monthly
Cloud free tier has no SLA
Slider movement does not save the last processed image

Limits

The cloud service's free tier includes 100 images per month, with unused quota not rolling over and no SLA. Cloud Auto Label All submits up to 20 images per request; over-limit requests return HTTP 402. Source builds need ONNX Runtime for local auto-labeling, though the app works without that feature.

Getting started

Download the prebuilt version for Windows x64, Linux x64, or macOS on Apple Silicon, or build from source with Qt 6.
Load .jpg or .png images from a directory.
Create bounding boxes with the two-click method and edit them as needed.
Export labels in YOLO TXT format.
For local auto-labeling, load a supported ONNX model; a source build needs ONNX Runtime for this feature.
For cloud detection, connect to yololabel.com with an API key and optionally provide a detection prompt.

Verdict

YoloLabel combines manual bounding-box annotation with local and cloud-assisted labeling options. Keep its unsaved-slider warning and the cloud service's monthly quota in mind.

Questions

Is YoloLabel free?

The desktop app is listed as free, and its repository is licensed under the MIT License. The connected cloud service has a free tier of 100 images per month.

Which platforms have prebuilt downloads?

Prebuilt desktop downloads are listed for Windows x64, Linux x64, and macOS on Apple Silicon.

What image formats can it load?

The README says it loads .jpg or .png images from a directory.

Which models can be used for local auto-labeling?

The README lists Ultralytics models exported to ONNX, including YOLOv5, YOLOv8, YOLO11, YOLO12, YOLOv26, and end-to-end ONNX models.

What is the cloud image quota?

The cloud free tier allows 100 images per month. Unused quota does not roll over, the tier has no SLA, and over-limit requests return HTTP 402.

Who makes YoloLabel?

The maker's GitHub profile identifies developer0hye as Yonghye Kwon.

Facts from YoloLabel's own pages

Annotation: It supports manual bounding box labeling and uses a two left-click method to create boxes. · 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
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
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
Cloud batch limit: Cloud Auto Label All submits images in batches of up to 20 per request. · github.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 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 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 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
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
Download platforms: The README lists prebuilt downloads for Windows x64, Linux x64, and macOS on Apple Silicon. · github.com, 30 Sept 2026
Downloads: Prebuilt desktop downloads are listed for Windows x64, Linux x64, and macOS Apple Silicon. · github.com, 30 Sept 2026
Image formats: The README says to load .jpg or .png images from a directory. · 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
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
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
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
Purpose: YoloLabel is a GUI for marking object bounding boxes in images to train YOLO neural networks. · 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
Support: The cloud service lists [email protected] as its contact email for questions about its terms and privacy policy. · yololabel.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
Usage caveat: The README warns that moving the horizontal image slider does not automatically save the last processed image. · github.com, 30 Sept 2026

Where YoloLabel stands on Freedom251

AI Image Annotation Tools#14 of 24