xisen-w

xisen-w / hl-imagenet

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Experimenting Heuristic Learning with ImageNet

47
3
100% credibility
Found May 13, 2026 at 47 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A demo of an AI coding agent iteratively building a symbolic image classifier for Tiny ImageNet classes using classical computer vision features and rules, reaching 86% development-set accuracy without neural networks.

How It Works

1
🔍 Discover HL-ImageNet

You stumble upon this fascinating project that classifies everyday objects like dogs, buses, and mushrooms using simple rules instead of fancy AI brains.

2
📥 Get it ready

Download the ready-to-use tool to your computer with a simple setup that takes just a minute.

3
📸 Add your photos

Drop in pictures of golden retrievers, school buses, teapots, or mushrooms from your collection or download a sample set.

4
🎯 Classify an image

Pick a photo and watch as the tool quickly figures out what it shows, giving a clear score like 'golden retriever 75% confident'.

5
💡 Read the reasoning

See a simple explanation of the clues it used, like 'warm brown fur in a green outdoor scene' or 'yellow body with sky above'.

6
📊 Test on many images

Run it on a whole folder to get accuracy stats and spot patterns in what it gets right or wrong.

✨ Master symbolic vision

You've built confidence in a transparent image classifier that explains itself, proving smart vision without mystery black boxes.

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Star Growth

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AI-Generated Review

What is hl-imagenet?

hl-imagenet is a Python project experimenting with heuristic learning to build a symbolic image classifier for Tiny ImageNet classes, ditching neural networks entirely for classical computer vision via OpenCV and hand-crafted rules. It processes 64x64 images, spits out predictions with human-readable proof traces explaining evidence like color blobs or textures, and hits 51-86% accuracy on validation sets depending on overlap. Users get a ready-to-run classifier via simple CLI commands like `hlinet-predict image.jpg` or evaluation scripts to benchmark on custom datasets.

Why is it gaining traction?

It stands out by delivering full explainability—every prediction lists firing features and absences—without black-box models, making it a fresh alternative to deep learning for low-res vision tasks. The built-in agent loop automatically proposes and tests new features from errors, showing real iterative improvement from random baseline to solid accuracy. Developers dig the no-ML-framework purity (just NumPy, SciPy, OpenCV) and plots tracking the learning trajectory.

Who should use this?

Vision researchers prototyping interpretable classifiers beyond gradients, or educators demoing symbolic AI on ImageNet subsets. Hobbyists tuning hand-crafted rules for embedded devices where NNs are overkill, or teams needing auditable predictions for regulated apps like medical imaging prototypes.

Verdict

Intriguing proof-of-concept for heuristic learning on ImageNet, but at 47 stars and 1.0% credibility, it's early-stage with Phase 2 still WIP—docs are solid via README and plots, but expect tuning for production. Worth forking for experiments if you crave NN-free vision.

(198 words)

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