Detect
Metered / request/v1/detectAnalyse one camera trap image and return detected animals with bounding boxes, labels, and taxonomy details.
Processing images regularly or at large scale?
Use this Instant detection endpoint for first tests, debugging, and small workflows. For steady pipelines, larger files, or bursty camera-fleet uploads, approved customers can use Detection jobs.
Open Detection jobsParameters
Required
Image data. Upload request body limit is 20 MB; raw image budget is 10 MB.
Optional
Optional geofencing hint (CCA2/CCA3/full country name).
Confidence threshold between 0.01 and 0.99. Default: 0.2.
Default: true. Set to false for detector-only animal, human, or vehicle labels.
When true, herd animals can use a precise label already found in the same image. Experimental: Denmark only.
Optional. Used only when longitude and country are also present. Adds area-level geofencing inside the country.
Optional. Used only when latitude and country are also present. Adds area-level geofencing inside the country.
Set to true to include available image metadata in the response.
Positive integer (1-10). With classify=true, returns alternative species candidates on each annotation.
Request notes
Send multipart/form-data for files or JSON with a base64 image. Use classify=false for faster coarse labels. Send latitude and longitude together; they only apply when country is also present. Limits: 20 MB request body and 10 MB raw image.
Status codes
Notes
- Best for: classical wildlife camera traps.
- Use
classify=falsefor faster blank filtering. Labels become coarse:animal,human, orvehicle. smooth_herd=truecan replace broad herd labels such asmammalwith a precise species already present in the image. Experimental: Denmark only.latitudeandlongituderequire each other andcountry. Without all three, area-level geofencing has no effect.metadata=trueincludes metadata only for fields found in the source image.top_candidatewithclassify=truecan add ranked alternative species to each annotation.- Use
/detect-urbanfor urban, indoor, roadside, zoo, farm, or other human-modified scenes.
Examples
const form = new FormData()
form.append('image', imageFile)
form.append('country', 'USA')
form.append('threshold', '0.2')
form.append('classify', 'true')
form.append('smooth_herd', 'true')
form.append('latitude', '56.834')
form.append('longitude', '9.994')
form.append('metadata', 'true')
form.append('top_candidate', '2')
const response = await fetch('https://api.animaldetect.com/v1/detect', {
method: 'POST',
headers: {
Authorization: 'Bearer ' + process.env.ANIMAL_DETECT_API_KEY,
},
body: form,
})
const data = await response.json(){
"id": "5e4e5dbd-2604-46b4-bb87-8f42fd682b08",
"expires_at": "2026-03-12T09:44:20.954Z",
"annotations": [
{
"id": 0,
"bbox": [0.41, 0.82, 0.20, 0.17],
"score": 0.997,
"label": "canine family",
"taxonomy": {
"id": "3184697f-51ad-4608-9a28-9edb5500159c",
"class": "mammalia",
"order": "carnivora",
"family": "canidae",
"genus": "",
"species": ""
},
"top_candidates": [
{
"label": "red fox",
"score": 0.18,
"taxonomy": {
"id": "11111111-1111-4111-8111-111111111111",
"class": "mammalia",
"order": "carnivora",
"family": "canidae",
"genus": "vulpes",
"species": "vulpes"
}
}
]
}
],
"metadata": {
"image_width": 4000,
"image_height": 3000,
"file_size": 2456789
},
"info": {
"processing_time_ms": 919,
"model_version": "mdv1000-speciesnet",
"model_id": "mdv1000-speciesnet",
"country_processed": "USA",
"threshold_applied": 0.2
}
}