Improve YOLO26 Crate Detection
Budget: €50 – €100 EUR
My current YOLO 26 model struggles with Eurobox and bread-crate detection, hovering below 50 % accuracy. With only ~100 training images (each holding 30–40 crates), I need to push performance past 94 % without relying on power-hungry cloud instances.
I’m open to every practical angle—tighter algorithmic tuning, smart preprocessing and creative data augmentation—so long as the final solution can run locally on a mid-range GPU or even CPU if possible. Feel free to experiment with lighter YOLO variants, pruning, quantisation, mosaic augmentation, rotation/flip tricks, colour tweaks or any other ideas you trust; I care about the end result and the ability to reproduce it on my hardware.
Acceptance criteria
• Provide the updated model weights, training script and a concise README.
• Demonstrate ≥94 % mAP (or equivalent class-level accuracy) on a hold-out set I’ll supply.
• Keep inference times reasonable for real-time (≤50 ms per image on RTX 3060 or similar).
If you can get me there efficiently, let’s talk—quick wins, clear metrics and clean code are what I’m after.
I’m open to every practical angle—tighter algorithmic tuning, smart preprocessing and creative data augmentation—so long as the final solution can run locally on a mid-range GPU or even CPU if possible. Feel free to experiment with lighter YOLO variants, pruning, quantisation, mosaic augmentation, rotation/flip tricks, colour tweaks or any other ideas you trust; I care about the end result and the ability to reproduce it on my hardware.
Acceptance criteria
• Provide the updated model weights, training script and a concise README.
• Demonstrate ≥94 % mAP (or equivalent class-level accuracy) on a hold-out set I’ll supply.
• Keep inference times reasonable for real-time (≤50 ms per image on RTX 3060 or similar).
If you can get me there efficiently, let’s talk—quick wins, clear metrics and clean code are what I’m after.