Object Detection

Opening — Why this matters now Drones have learned to fly cheaply, see broadly, and deploy everywhere. What they still struggle with is something far less glamorous: noticing small things that actually matter. In aerial imagery, most targets of interest—vehicles, pedestrians, infrastructure details—occupy only a handful of pixels. Worse, they arrive blurred, partially occluded, and embedded in visually noisy backgrounds. Traditional object detectors, even highly optimized YOLO variants, are structurally biased toward medium and large objects. Small objects are the first casualties of depth, pooling, and aggressive downsampling. ...

Open-vocabulary object detection — the holy grail of AI systems that can recognize anything in the wild — has been plagued by fragmented training strategies. Models like OWL-ViT and Grounding DINO stitch together multiple learning objectives across different stages. This Frankensteinian complexity not only slows progress, but also creates systems that are brittle, compute-hungry, and hard to scale. Enter OmniTrain: a refreshingly elegant, end-to-end training recipe that unifies detection, grounding, and image-text alignment into a single pass. No pretraining-finetuning sandwich. No separate heads. Just a streamlined pipeline that can scale to hundreds of thousands of concepts — and outperform specialized systems while doing so. ...