YoloLabel

Input—per 1M tokens

Output—per 1M tokens

Context—tokens

WeightsClosed

About

YoloLabel is ranked #3 of 24 in AI image annotation tools on Inferse. It runs on API, Linux, macOS, Self-hosted, Windows.

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