🔥 KeenForgeAI

Forging high-quality, niche vision datasets — and the open-source tool to build them.

KeenForgeAI is an open-source organization focused on re-annotating and curating small, high-quality niche datasets for computer vision, and on building KeenForge — a local-first annotation & training desktop tool that makes this kind of careful work practical.

🔗 GitHub: https://github.com/KeenForgeAI/KeenForge · Datasets: https://huggingface.co/KeenForgeAI

🛠 The tool: KeenForge

CVPR 2026 Demo Track · Formerly AutoLabel Pro

KeenForge is a desktop application that lets you train your own object-detection model without writing a single line of code. Import images, draw a few boxes, and the AI trains itself in the background. Your data never leaves your computer.

	KeenForge
Deployment	Double-click an `.exe` (Windows)
Privacy	100% local & offline — images are never uploaded
AI assistance	Built-in YOLO-World zero-shot detection
Auto-training	Triggers automatically after ~15 labels
Model ownership	You own your model
Price	Free & open source (MIT)

Core features

Zero-shot cold start — type any object name ("welding defect", "safety helmet", "platelet") and KeenForge finds it immediately. No pre-training required.
Self-looping active learning — label ~15 images → auto-train in the background → the model suggests better boxes on the next images → repeat. The more you label, the smarter it gets.
Infinite canvas — smooth pan / zoom for large, high-resolution images.
Export anywhere — YOLO / COCO / VOC formats.

🎬 Watch the demo · ⭐ Star on GitHub

🎯 Our mission: high-quality niche datasets

Most public datasets are large but noisy. Smaller well-annotated datasets in niche domains are often the ones that actually move research forward — yet they are the hardest to find.

We take public niche datasets — industrial / manufacturing, medical & microscopy, agriculture, safety, and more — and:

Re-annotate them carefully with a human-in-the-loop workflow in KeenForge (AI pre-labels → every image reviewed by a human → independent cross-check by a second annotator).
Clean them — tighten boxes, fix mislabels, add missed instances, remove duplicates.
Document them — full provenance, source datasets, correction reports and citations.
Publish them openly on Hugging Face and ModelScope.

Published datasets

Dataset	Domain	Size	Description
`GC10-DET-corrected`	🏭 Industrial (steel)	2,280 images · 3,542 boxes	Hot-rolled steel strip surface defects, 10 classes (Pascal VOC) — cleaned version of GC10-DET
`TXL-PBC-corrected`	🩸 Medical (hematology)	1,256 images · 18,098 boxes	Peripheral blood cell detection (WBC / RBC / Platelets) — corrected version of TXL-PBC
`raccoon-corrected`	🦝 Wildlife	193 images · 211 boxes	Fully re-annotated version of the classic Raccoon detection dataset

More coming — PCB, agriculture, safety, …

💡 What we believe

Only high-quality data can push visual AI forward.

Architectures are converging and compute keeps getting cheaper — but a model is only ever as good as the labels it learns from. We optimise for annotation quality, provenance and reproducibility, not raw size.

🤝 Join us

KeenForgeAI is a community effort. You can help by:

Use KeenForge — report bugs or request features in the issue tracker.
Contribute code — MIT-licensed; PRs, docs and translations welcome.
Contribute datasets — re-annotated a niche dataset? Open an issue and we'll help you publish it.
Spread the word — star the repo and share your workflow.

The best vision datasets will come from many small communities working carefully, not from a few giant scrapes. Come build with us.

中文版

KeenForgeAI 是一个开源组织，专注于「高质量小众视觉数据集」的重新标注与整理，并开发配套的本地化标注训练工具 KeenForge。

🛠 工具：KeenForge

CVPR 2026 Demo Track · 前身为 AutoLabel Pro

KeenForge 是一款桌面应用，无需写一行代码就能训练你自己的目标检测模型：导入图片 → 画几个框 → AI 在后台自动训练。数据全程不离开你的电脑。

零样本冷启动：直接输入要检测的目标名称（如「焊接缺陷」「安全帽」「血小板」），内置 YOLO-World 立即识别，无需预训练
自循环主动学习：标注约 15 张图 → 后台自动训练 → 模型在新图上给出更好的框 → 继续标注，越标越准
无限画布：大图 / 高分辨率图片流畅缩放平移
一键导出：YOLO / COCO / VOC 格式
100% 本地离线 · 模型归你所有 · 完全免费开源（MIT）

🎬 演示视频｜ ⭐ GitHub 仓库

🎯 我们的使命

公开数据集往往「大而糙」——框松、漏标、错标、重复。真正推动研究的，常常是那些小众领域里标注精良 的小数据集，但它们恰恰最难找。

我们把公开的小众数据集（工业制造、医疗显微、农业、安防等）重新整理：

用 KeenForge 的人机协同流程重新精标（AI 预标注 → 逐张人工复核 → 第二人交叉复核）
清理：收紧框、改错标、补漏标、去重复
完整文档化：来源、修正报告、引用信息齐全
开源发布到 Hugging Face 与 ModelScope

已发布数据集：🏭 GC10-DET-corrected（工业 — 钢板表面，2,280 张 / 3,542 框）、🩸 TXL-PBC-corrected（医疗 — 血液学，1,256 张 / 18,098 框）、🦝 raccoon-corrected（野生动物，193 张 / 211 框）

💡 我们的理念

只有高质量的数据集，才能推动视觉 AI 的发展。

模型架构在趋同、算力越来越便宜，但模型的上限永远由标注质量决定。我们死磕的是标注质量、可溯源性和可复现性，而不是数据量。

🤝 加入我们

用 KeenForge——提 issue / 提需求
贡献代码（MIT 许可，欢迎 PR、文档、翻译）
贡献数据集——用 KeenForge 重新标注了小众数据集？开个 issue，我们帮你发布
帮忙传播——给仓库点个 star，分享你的工作流

我们相信，未来最好的视觉数据集会来自许多小社区认真细致的工作，而不是少数几个巨大的爬取。 欢迎一起共建 KeenForgeAI。

🔗 GitHub: https://github.com/KeenForgeAI/KeenForge ｜ Hugging Face: https://huggingface.co/KeenForgeAI