1/ Today we're shipping Perceptron Agentic Detection. Describe what you want in natural language, or show one example crop, and an agent grounds it in the image. No fine-tuning, no fixed class list.
Today we're releasing Perceptron Agentic Detection: localize anything you can describe in natural language or show examples of.
Some users call Perceptron's agentic detection for natural language image localization genuinely impressive because of its strong performance on tasks like finding snipers.
One of the cool emergent properties was the ability to solve a non-trivial percentage of r/FindTheSniper problems. These are complex perceptive questions that humans struggle with as well. Examples below.
1/ Today we're shipping Perceptron Agentic Detection. Describe what you want in natural language, or show one example crop, and an agent grounds it in the image. No fine-tuning, no fixed class list.
2/ We stopped treating detection as a single forward pass. The harness zooms, tiles, reasons and requeries until the question is resolved, and on our benchmarks that approach comes out ahead of Gemini, Qwen, and raw Mk1.
1/ Today we're shipping Perceptron Agentic Detection. Describe what you want in natural language, or show one example crop, and an agent grounds it in the image. No fine-tuning, no fixed class list.
3/ Geospatial has been a big test bed for this. Some additional anecdotal samples. Left Perceptron, right Gemini.
2/ We stopped treating detection as a single forward pass. The harness zooms, tiles, reasons and requeries until the question is resolved, and on our benchmarks that approach comes out ahead of Gemini, Qwen, and raw Mk1.
6/ Building this surprised us in one specific way. Once the model controls where it looks, it goes past detection: the loop reads what it finds and connects it to what it knows about the world, and behaviors show up that base model evals never indicated.
5/ Don't have a clean way to describe the object, use visual exemplars.
Can you find the snake?
One of the cool emergent properties was the ability to solve a non-trivial percentage of r/FindTheSniper problems. These are complex perceptive questions that humans struggle with as well. Examples below.
4/ It handles dense scenes the same way. We gave both models Brisbane Airport zero-shot. Single-pass detection has trouble at this density.
3/ Geospatial has been a big test bed for this. Some additional anecdotal samples. Left Perceptron, right Gemini.
The harness isn't done. The agent is verbose and slower than I'd like, both fixable. Lots of good research left in visual reasoning, and we'll be landing it soon.
Can you find the snake?
How about the bird?
5/ Don't have a clean way to describe the object, use visual exemplars.
4/ It handles dense scenes the same way. We gave both models Brisbane Airport zero-shot. Single-pass detection has trouble at this density.
8/ We're not publishing that example. For now, we are keeping this category off the public API and are working with a trusted set of partners on this domain.
7/ The clearest case came from an internal eval on a long drone video over a conflict zone. The model zoomed on a small door sign, read it, understood what the sign implied about the building's contents (i.e., POL), and flagged the structure as an optimal target for the drone.
9/ Everything else is live today. Try it: http://perceptron.inc/demo?mode=detect Blog: http://perceptron.inc/blog/introducing-perceptron-agentic-detection
@ArmenAgha the find the sniper thing is genuinely impressive tbh
makes me wonder what else its accidentally good at
1/ Today we're shipping Perceptron Agentic Detection. Describe what you want in natural language, or show one example crop, and an agent grounds it in the image. No fine-tuning, no fixed class list.
Today we're releasing Perceptron Agentic Detection: localize anything you can describe in natural language or show examples of.