Can you spot what the AI missed? Red boxes = human labels. Blue boxes = my model’s predictions. The baseline finds obvious road damage but misses some smaller/distant potholes. Next: test it on Lagos roads. #BuildInPublic#ComputerVision
@aksenHQ I’ll add real Lagos images of small/distant potholes with consistent boxes, varied lighting/weather and look-alike negatives. I’ll test on held-out routes. If a pothole is only a few pixels, I’ll treat it as beyond reliable detection rather than label noise.
First baseline done for my Lagos pothole project using Aegis’s 1,482-image public dataset. YOLO26n, 5 epochs. Test mAP50: 0.851. It detected obvious damage but missed some smaller/distant potholes. Next: Lagos road tests. #BuildInPublic
@progerallworld That’s a great point—thank you. For my Lagos dataset, I’ll split by street/route or video, not by individual frames, so similar frames from the same road segment stay on one side. That should give a more honest test of generalization.
I’m starting a Lagos pothole-detection project. I’ll test whether AI can mark possible potholes in road images, then record their locations. I’ll share results, mistakes and lessons as I learn. #BuildInPublic#ComputerVision
@aksenHQ I’m starting with the Pothole Detection dataset by Aegis on Roboflow Universe—1,482 images, CC BY 4.0—for my baseline. Later, I’ll test on Lagos road images and build a local dataset for fine-tuning.