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 )
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In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.
#5 best model for Panoptic Segmentation on COCO panoptic
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).
#142 best model for Image Classification on ImageNet
This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices.
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.
In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera.
We propose Mnasfpn, a mobile-friendly search space for the detection head, and combine it with latency-aware architecture search to produce efficient object detection models.
#78 best model for Object Detection on COCO test-dev