https://paperswithcode.com/task/object-detection
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.
每一个不曾起舞的日子,都是对生命的辜负。
But it is the same with man as with the tree. The more he seeks to rise into the height and light, the more vigorously do his roots struggle earthward, downward, into the dark, the deep - into evil.
其实人跟树是一样的,越是向往高处的阳光,它的根就越要伸向黑暗的地底。----尼采