# LinkedIn Post Draft

I built a small experiment around representation-first operational vision with I-JEPA.

The idea is simple:

YOLO gives precise object labels and boxes.
I-JEPA gives frozen visual representations that can be probed for scene structure, context, and approximate semantic similarity.

So instead of treating I-JEPA as a detector, I used it as a representation layer:

- YOLO boxes as benchmark labels
- I-JEPA patch saliency as a rough "where is visual structure strongest?" signal
- class prototypes from object-crop embeddings
- a tiny LogisticRegression head trained on frozen I-JEPA embeddings
- object/context/scene similarity to reason about whether something is isolated, embedded, or part of a group-like scene

What I like about the tiny head: it can be only tens of thousands of trainable parameters, while the large I-JEPA model stays frozen. If that small layer can classify objects from embeddings, the representation is doing most of the heavy lifting.

One interesting observation: rare classes such as manholes were weak with only a few prototype support samples, but became much more recognizable as support coverage increased. That is a nice reminder that representation quality and support coverage interact.

This is not just about replacing YOLO box-for-box. It is a practical probe into representation-first vision:

Can frozen self-supervised models help us understand both objects and the surrounding scene context, with only a tiny classifier on top?

Repo / demo:
<add link here>

#AI #ComputerVision #HuggingFace #SelfSupervisedLearning #JEPA #OperationalAI