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Locate in Image (Object Detection with Bounding Boxes)
x402
Locate in Image (Object Detection with Bounding Boxes) is a paid API for AI agents from agent402.tools, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-29).
Detects up to 4 specified objects in a single image, returning presence probability and normalized bounding box coordinates for each target.
Price / call$0.002
Success—
StatusUnknown
How this service works
Find up to 4 things in one image: each comes back with whether it is there (a probability) and its box, as edges from 0 to 1. Preview: built on Jev features its maker labels experimental. Answers come from a model, not deterministic code.
Output
For each of the up to 4 specified target objects, the endpoint returns a probability score indicating likelihood of presence and normalized bounding box coordinates (edges expressed as values between 0 and 1 relative to image dimensions), allowing precise spatial localization within the image.
Example use cases
Product placement verification in ad photos
“I have a PNG screenshot of our ad creative — can you check whether 'the brand logo', 'the product bottle', and 'the call-to-action button' are all visible, and tell me where each one appears in the image?”
Safety check for construction site images
“Here's a JPEG photo from our construction site. Can you detect whether 'the hard hat', 'the safety vest', and 'the warning cone' are present and give me their positions?”
Automated retail shelf audit
“I'm uploading a JPEG image of a store shelf — please find 'the cereal box', 'the price tag', and 'the shelf label' and tell me the probability each one is there along with their bounding boxes.”
Technical details
Endpoint
POSThttps://agent402.tools/v1/judge/locate?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both presence detection and spatial localization (bounding boxes) for a small set of named objects in a single image without running your own computer vision infrastructure. It is ideal for lightweight, pay-per-call object detection tasks where you need normalized box coordinates (0–1 scale) for up to 4 targets at once. Prefer alternatives if you need to detect more than 4 objects, require pixel-perfect deterministic results, or need to process large batches of images efficiently.
Side effects
- Image dimensions outside 256–1568px per side will be rejected
- More than 4 targets specified will be refused
- Unsupported image format (not PNG or JPEG) causes an error
- Model may return low-confidence results for ambiguous or occluded objects — probabilistic, not deterministic
- Base64 encoding errors or malformed image data will cause a parsing failure
- Network timeouts possible for large images
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| image | object | { format: "png" | "jpeg", data_base64 }; each side 280 to 1568 px. |
| targets | array | What to find, up to 4, for example "the coffee mug". |
Tags
x402
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