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This repository covers the complete workflow of object detection using Detectron2. It includes theoretical understanding, dataset annotation and COCO conversion, model training and evaluation, inference on new images, and an end-to-end wildlife monitoring pipeline addressing real-world challenges like occlusion and low-light detection.

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Detectron2 is Meta AI’s PyTorch-based library for state-of-the-art object detection and segmentation, offering a modular design and rich model zoo for research and production. TFOD2 is Google’s TensorFlow 2-based framework that provides pre-trained detection models (SSD, Faster R-CNN) and utilities for training, evaluation, and deployment.