output_format.md

title	MegaDetector Output Format, detection JSON schema
description	MegaDetector output format: the JSON schema for detection results, file path, category, confidence score, and bounding box, and how to use it downstream.
tags	megadetector output json detection schema bounding box format camera trap AI megadetector results

Output Format

MegaDetector writes its results as JSON. Whether you run the megadetector CLI or the Python API, the structure is the same: a list of per-image records, each holding the image path and the objects detected in it.

Schema

The top level is a JSON list. Each element describes one image:

[
  {
    "file": "images/IMG_0001.jpg",
    "detections": [
      { "category": "animal",  "confidence": 0.93, "bbox": [102.4, 88.1, 540.7, 470.2] },
      { "category": "person",  "confidence": 0.81, "bbox": [12.0, 40.5, 96.3, 510.9] }
    ]
  },
  {
    "file": "images/IMG_0002.jpg",
    "detections": []
  }
]

An image with no detections above the threshold has an empty detections list, that is how blank frames are represented.

Fields

Field	Type	Description
file	string	Path to the image, as supplied to the detector
detections	list	One object per detection kept above the threshold (empty if none)
detections[].category	string	animal, person, or vehicle
detections[].confidence	float	Detection confidence from 0 to 1, rounded to 4 decimals
detections[].bbox	list of 4 floats	Bounding box as [x1, y1, x2, y2] (top-left and bottom-right pixel coordinates), rounded to 1 decimal

The category names map from the model's class IDs: 0 → animal, 1 → person, 2 → vehicle.

Thresholding

Only detections whose confidence is greater than or equal to your chosen threshold are written; everything below is dropped. The CLI default is 0.2, and most projects tune within 0.15–0.3 for the animal category. Lowering the threshold favors recall (fewer missed animals) at the cost of more false positives; raising it does the reverse.

Using the output

The JSON is designed to flow straight into the rest of a camera-trap workflow:

• Review, import it into a tool such as Timelapse to verify and annotate detections (see Camera-Trap Software).
• Filter, drop images whose detections list is empty to clear blank frames in bulk.
• Classify, crop each bbox and pass it to a species classifier (see the two-stage pipeline in the FAQ).

Next steps

• CLI reference: the detect command that produces this JSON
• Camera-Trap Software: review and analysis tools that consume it
• Model Zoo: choose the variant that produced your detections