Detect
1 credit / requestPOST /v1/detectAnalyze one camera-trap image and return detected animals with bounding boxes, labels, and taxonomy details.
Processing images regularly or at large scale?
Use this real-time endpoint for first tests, debugging, and small workflows. For steady pipelines, larger files, or bursty camera-fleet uploads, approved customers can use Batch Processing.
Open Batch ProcessingExamples
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
}
}Parameters
Required
Image data. Upload request body limit is 20MB; raw image budget is 10MB.
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: 20MB request body and 10MB raw image.
Status Codes
Notes
- Best for: classical wildlife camera traps.
- Use `classify=false` for faster blank filtering. Labels become coarse: `animal`, `human`, or `vehicle`.
- `smooth_herd=true` can replace broad herd labels such as `mammal` with a precise species already present in the image. Experimental: Denmark only.
- `latitude` and `longitude` require each other and `country`. Without all three, area-level geofencing has no effect.
- `metadata=true` includes metadata only for fields found in the source image.
- `top_candidate` with `classify=true` can add ranked alternative species to each annotation.
- Use `/detect-urban` for urban, indoor, roadside, zoo, farm, or other human-modified scenes.