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#2 Detectron2
Facebook AI Research's modular detection library offering state‑of‑the‑art Faster/Mask R‑CNN, RetinaNet, and DensePose models with flexible configuration.
Google's open‑source API with a wide catalog of pre‑trained models (SSD, Faster R‑CNN, EfficientDet) and easy integration into TensorFlow pipelines.
#4 MMDetection
OpenMMLab's comprehensive toolbox supporting dozens of detection algorithms (YOLO, RetinaNet, Cascade R‑CNN) with a unified training/evaluation framework.
#5 EfficientDet
Google‑Brain's scalable detector that balances accuracy and latency using compound scaling of backbone, feature network, and box/class heads.
A fast, anchor‑based detector that runs efficiently on mobile and embedded devices, with implementations in Caffe, PyTorch, and TensorFlow.
#7 Faster R‑CNN
Two‑stage region proposal network offering high detection accuracy, widely used as a baseline for many research projects.
#8 RetinaNet
One‑stage detector that introduces focal loss to address class imbalance, delivering high AP while remaining relatively fast.
#9 CenterNet
Keypoint‑based object detector that predicts object centers and sizes, achieving competitive speed on CPUs and GPUs.
#10 Mask R‑CNN
Extends Faster R‑CNN to perform instance segmentation as well as bounding‑box detection.
Cross‑platform inference engine supporting models from Caffe, TensorFlow, ONNX, and Darknet, ideal for lightweight deployment.
#12 DeepStream SDK
NVIDIA's real‑time video analytics framework optimized for Jetson and GPU servers, supporting YOLO, SSD, Faster R‑CNN, etc.
Fully managed AWS service offering object, scene, and activity detection with easy API integration.
Google's cloud service for image labeling, object detection, and OCR, accessible via REST or client libraries.
Azure's AI service providing object detection, tagging, and spatial analysis with built‑in model updates.
IBM's cloud‑based visual classification service that can be trained on custom datasets for object detection.
#17 MobileNet‑SSD
Lightweight detection model combining MobileNet backbone with SSD heads, perfect for mobile and edge devices.
#18 YOLOv5
Ultralytics' PyTorch implementation of YOLO, widely adopted for its simplicity, extensive documentation, and active community.
#19 YOLOv7
Latest community‑driven YOLO variant offering improved accuracy‑speed trade‑offs and new training tricks.
Unified framework supporting detection, segmentation, and classification with a single API.
Vision‑Transformer based detector that leverages hierarchical Swin architecture for high‑performance detection.