yolo-cls – C++ batch image classification CLI with ONNX Runtime

Hacker News

Hi HN, I'm Savelii, homelab enthusiast and open source software supporter. I've built a command-line tool written in C++ for performing fast, multi-threaded image classification using YOLO models. It uses the ONNX Runtime as its inference backend. For more than 10 years, I've been hoarding different assets on my servers: family photos, project showcases, scientific results, etc. Recently, I needed to find photos containing a specific object but didn't want to manually look through every photo via SAMBA. So I decided to automatically classify every photo and then just sort the results. Even though there are many great Python tools for this, I wanted something lightweight with minimal dependencies that I could easily script and integrate directly into shell commands. My goal was to create a tool that felt like a native part of a Unix-style workflow, where you can easily do things like `find . | yolo-cls ... | grep -e "dragonfly"`. Features: 1. Simplicity and performance - written in C++, it can be bundled as a single executable file and is designed to do one thing well. 2. CLI and Unix philosophy - it requires no GUI and accepts file paths from arguments or from `stdin`, making it highly composable with other tools like `find`, `grep`, and `ls`. 3. Cross-platform - thanks to the ONNX Runtime, the tool can be used on Linux (x64, aarch64) and Windows. The project is open source (GNU GPL v3.0). I welcome all feedback and collaboration from the community. The source code, build instructions, and pre-compiled binaries are available on GitHub: https://github.com/SavaLione/yolo-cls Thanks for taking a look!

Visit product View on Hacker NewsAug 24, 2025

Share card

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, single · Missing: mac, agents, macos

85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

No more batch image resizing13%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

No more batch image resizing

Hacker News2

Didder – CLI Image Dithering32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Didder – CLI Image Dithering

Hacker News1

BIRME38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Batch Image Resizing Made Easy

Indie Hackerscommitment-side-project

A container runtime implemented in x86_64 assembly55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A container runtime implemented in x86_64 assembly

Hacker News3

Subverting Go's Runtime System47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Subverting Go's Runtime System

Hacker News2

Toggle Methods and Endpoints at Runtime48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Toggle Methods and Endpoints at Runtime

Hacker News2

Mixing C++ with Angelscript for runtime introspection and debugging59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Mixing C++ with Angelscript for runtime introspection and debugging

Hacker News1

Restate, a Distributed Async Runtime55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Restate, a Distributed Async Runtime

Hacker News2

AegisBPF – Deterministic Runtime Enforcement via eBPF LSM35%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

AegisBPF – Deterministic Runtime Enforcement via eBPF LSM

Hacker News2

Kern – container and resource runtime in a 1.5 MB binary, no daemon43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Kern – container and resource runtime in a 1.5 MB binary, no daemon

Hacker News70

Browse all products Analyze your launch