u/LensLaber

I'm tired of web platforms forcing us to upload everything to the cloud. So, I built LensLaber, an offline-first computer vision annotation tool.

I developed the whole thing on my everyday laptop: an old 2015 Asus X550LD (i5, 8GB RAM, 120GB SSD). I wanted to prove you don't need an expensive GPU workstation for AI labeling. By optimizing the architecture, YOLO + MobileSAM run locally on standard CPU using just 600-900MB of RAM. You just bring your own YOLO weights in ONNX format.

Harry Ratcliffe (Applied AI Ecosystem Leader) reviewed the architecture and was mostly surprised by how smoothly both models run on this 2015 hardware. He validated that this local, low-RAM setup is exactly what high-security sectors like medical imaging or manufacturing actually need.

Now I need honest testing, not casual "looks nice" comments. Whether you're a professional managing sensitive enterprise data or a student working on a class project, I need people to actually run real datasets through it. That’s the only way to see how the UI handles real-world friction.

Your time matters. If you provide active feedback during this beta, I’ll give you a lifetime free license for the final release.

Download the beta here:https://lenslaber.github.io

I'll be hanging around the comments to fix any bugs you find. Let me know your thoughts.

I've created a computer vision annotation tool that runs completely offline on a 2015 laptop, using YOLO and MobileSAM on the CPU only. The Windows beta has been available for a while, and I just released the first AppImage for Linux.

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I started this project because I was tired of tools that require a powerful GPU or upload data to the cloud to use auto-labeling features. I wanted something that could run locally on modest hardware.

The entire engine was developed on a laptop with a 4th-generation i5 and 8 GB of RAM. It runs YOLO ONNX and MobileSAM entirely on the CPU for semi-automatic segmentation. It typically consumes between 600 and 900 MB of RAM, even with datasets containing more than 20,000 images.

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Key Features:

- Works completely offline

- YOLO tagging support

- MobileSAM segmentation

- CPU optimized

- Compatible with Windows and Linux

- Supports large datasets

Before you download:

- Current beta versions expire after 30 days. This is not a limited trial or a subscription. I release updates frequently and don't want to waste time debugging bugs in older versions that have already been fixed. When your beta expires, simply download the latest beta from the repository.

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The software will be paid when the stable version is released. However, anyone who actively participates in testing and provides helpful feedback will receive a free lifetime license for V1.

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I've also included VirusTotal reports for Windows and Linux in the repository, so anyone can verify the binaries before running them.

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https://github.com/LensLaber/LensLaber.github.io

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If you try it, I'd like to know what's wrong, what's inconvenient, and what I should improve.

Hello everyone,

I've been working on this project for quite some time because I was tired of modern annotation tools. It seems like every program these days assumes you have unlimited RAM, a high-end GPU, or a constant, high-speed cloud connection.

To push my optimization limits, I forced myself to build the entire project on my old laptop: a 2014 ASUS X550LD (Intel i5-4200U, 8 GB of RAM, and a practically unusable GeForce 820M).

The result is LensLaber, an offline annotation tool for computer vision datasets that runs automated detection and segmentation workflows locally on a very basic machine. RAM usage is kept strictly between 600 and 900 MB, even with MobileSAM running on the CPU.

100% Offline Operation: No cloud dependency, no uploads, no internet connection required. Your data never leaves your machine.

Local AI assistance: YOLO ONNX inference (using your own models) + integrated MobileSAM polygon generation, running efficiently on the CPU.

Comprehensive workflow: Dataset quality inspection, false negative detection and review, advanced filtering, data augmentation, and export to COCO. I wanted to stop switching between annotation tools and custom Python scripts just to clean a dataset.

I use the tool myself with real datasets almost daily, so development is primarily based on the problems I encounter in my work.

The beta version is completely free, with a 30-day limit, but this is simply to ensure you always use the latest updated beta. When the final version is released, all active testers on the project will receive a completely free and unrestricted license. I would love to receive your honest feedback, especially if you work with large datasets on modest hardware or if you value strict data privacy.

GitHub and download: https://github.com/LensLaber/LensLaber.github.io