Models that understand the details
Our models extract rich information from your videos, including the characteristics and history of the objects in them.
Transit lane sign
- Visible text
- RIGHT LANE · BUSES TAXIS ONLY
- Location
- 1000 block Mission StBetween 6th & 7th · right sideApprox. GPS 37.7800, -122.4101
A working model in minutes
Your coding agent can define and build Dragoneye models that work out of the box, without data labeling or model training.
Describe what you want to detect
Count vehicles by type, including cars, pickup trucks, vans, buses, motorcycles, and trucks.
Your agent writes the model schema
Use Claude Code or Codex with the Dragoneye MCP server, or the agent in our Playground. Define what your model should detect.
intersection_vehicles- Passenger car
- Pickup truck
- Van
- Bus
- Motorcycle
- Truck
Dragoneye builds your model
Dragoneye builds your model from the schema. Ready in minutes, with no training data or labeling.
recognize_anything/intersection_vehicles Ready in minutes·No training data or labeling
Run the model on your video
Run the model in a few lines with our Python or Node SDK.
import asyncio from dragoneye import Dragoneye, Video client = Dragoneye() result = asyncio.run(client.classification.predict_video( media=Video.from_path("intersection.mp4"), model_name="recognize_anything/intersection_vehicles", )) objects = result.objectsimport { Dragoneye } from "dragoneye-node"; const client = new Dragoneye({ apiKey: process.env.DRAGONEYE_API_KEY }); const video = await Dragoneye.Video.fromFilePath( "intersection.mp4" ); const result = await client.classification.predictVideo( video, "recognize_anything/intersection_vehicles" ); console.log(result.objects);from dragoneye import Dragoneye, Video client = Dragoneye() video = Video.from_path("intersection.mp4") result = await client.classification.predict_video( media=video, model_name="intersection_vehicles" )import { Dragoneye, Video } from "dragoneye-node"; const client = new Dragoneye(); const video = await Video.fromFile("intersection.mp4"); const result = await client.predictVideo( video, "intersection_vehicles" );
Review the results
| Object | Vehicle type | First seen | Last seen | Confidence |
|---|---|---|---|---|
| V-001 | Van | 00:13.6 | 00:20.0 | 0.98 |
| V-002 | Van | 00:17.2 | 00:19.6 | 0.97 |
| V-003 | Passenger car | 00:17.5 | 00:20.0 | 0.96 |
| V-004 | Truck | 00:15.8 | 00:20.0 | 0.98 |
| V-005 | Passenger car | 00:18.6 | 00:20.0 | 0.97 |
Adapt as your needs change
Update your model to extract the new information your use case needs.
Describe what’s changed
Record each vehicle’s exterior color and make.
Update the schema. Dragoneye rebuilds.
Add attributes to the schema, then send the update to Dragoneye.
intersection_vehicles- Passenger car
- Pickup truck
- Van
- Bus
- Motorcycle
- Truck
- Exterior color
- White, Silver, Blue, …
- Make
- Ford, Toyota, Mercedes-Benz, …
recognize_anything/intersection_vehicles Ready in minutes·No training data or labeling
The new data flows in
Run the rebuilt model on the same video. Exterior color and make now appear alongside each tracked vehicle.
| Object | Vehicle type | First seen | Last seen | Confidence | Exterior color | Make |
|---|---|---|---|---|---|---|
| V-001 | Van | 00:13.6 | 00:20.0 | 0.98 | White | Ram |
| V-002 | Van | 00:17.2 | 00:19.6 | 0.97 | Black | Toyota |
| V-003 | Passenger car | 00:17.5 | 00:20.0 | 0.96 | Black | Ford |
| V-004 | Truck | 00:15.8 | 00:20.0 | 0.98 | Light blue | Mack |
| V-005 | Passenger car | 00:18.6 | 00:20.0 | 0.97 | Black | Toyota |
Models that adapt across environments
Pretrained on a broad range of visual data, our zero-shot models adapt to new environments and use cases without task-specific labeling or training.
Airport operations
Track ground equipment
- Equipment
- Cargo loader
- Bag unload
- Open
Traffic surveys
Count movements by vehicle type
- Vehicle type
- Van
- Westbound · straight
- 57 vehicles
Sponsor exposure
Measure logo exposure
- Brand
- OpenAI
- On screen so far
- 3.80 seconds
Roadside inventory
Catalog roadside assets
- Asset type
- No-stopping sign
- Side of road
- Left
Easy to integrate into your product
Use our Python or Node SDK to connect model results to your alerts, reports, and workflows.

ground_equipment
intersection_vehicles
roadside_assetsresults
Use model
results.
Bring detections into
your application.

See what’s possible with your footage
Try it on your own videos, or discuss your use case with us.
Try it on
your videos
Use the Playground to create a model and test your footage.
Try it on your videosDiscuss
your use case
Tell us what you need from your videos. We’ll talk through whether Dragoneye can help.
Discuss your use case