MegaDetector is a production-grade object detection pipeline specifically designed for camera trap images in wildlife conservation. It detects animals, people, and vehicles in images and videos, outputting bounding boxes with confidence scores in a standardized JSON format (MegaDetector output format). The system provides a comprehensive set of command-line tools for batch inference, post-processing (repeat detection elimination, classification smoothing, format conversion), and integration with platforms like Wildlife Insights and LILA. The architecture is a modular Python package with separate modules for detection (supporting PyTorch and TensorFlow backends), classification, data management, post-processing, visualization, and taxonomy mapping. Key entry points include `run_detector_batch.py` for batch inference, `run_detector.py` for single-image inference, `postprocess_batch_results.py` for generating HTML previews, and `separate_detections_into_folders.py` for organizing results. Data flows from raw images through the detector to JSON results, which are then post-processed (e.g., false positive reduction, confidence thresholding) and optionally converted to other formats (COCO, YOLO, Wildlife Insights). External dependencies include PyTorch, TensorFlow, ultralytics, OpenCV, PIL, and various utility libraries. The repository also includes a C# Windows GUI application for JSON subsetting and integration guides for Timelapse and digiKam.