https://paperswithcode.com/task/object-detection
About
Object detection is the task of detecting instances of objects of a certain class within an image. The state-of-the-art methods can be categorized into two main types: one-stage methods and two stage-methods. One-stage methods prioritize inference speed, and example models include YOLO, SSD and RetinaNet. Two-stage methods prioritize detection accuracy, and example models include Faster R-CNN, Mask R-CNN and Cascade R-CNN.
The most popular benchmark is the MSCOCO dataset. Models are typically evaluated according to a Mean Average Precision metric.
( Image credit: Detectron )
Benchmarks
Greatest papers with code
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
We present a class of efficient models called MobileNets for mobile and embedded vision applications.
Ranked #113 on Object Detection on COCO test-dev
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
MobileDets also outperform MobileNetV2+SSDLite by 1. 9 mAP on mobile CPUs, 3. 7 mAP on EdgeTPUs and 3. 4 mAP on DSPs while running equally fast.