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Object Detection

732 papers with code · Computer Vision

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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Greatest papers with code

Objects as Points

16 Apr 2019tensorflow/models

We model an object as a single point --- the center point of its bounding box.

KEYPOINT DETECTION REAL-TIME OBJECT DETECTION

Pooling Pyramid Network for Object Detection

9 Jul 2018tensorflow/models

We share box predictors across all scales, and replace convolution between scales with max pooling.

OBJECT DETECTION

Focal Loss for Dense Object Detection

ICCV 2017 tensorflow/models

Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.

DENSE OBJECT DETECTION REAL-TIME OBJECT DETECTION REGION PROPOSAL

Speed/accuracy trade-offs for modern convolutional object detectors

CVPR 2017 tensorflow/models

The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform.

OBJECT DETECTION

Group Normalization

ECCV 2018 facebookresearch/detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

OBJECT DETECTION VIDEO CLASSIFICATION

Data Distillation: Towards Omni-Supervised Learning

CVPR 2018 facebookresearch/detectron

We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data.

KEYPOINT DETECTION OBJECT DETECTION

Non-local Neural Networks

CVPR 2018 facebookresearch/detectron

Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time.

#7 best model for Keypoint Detection on COCO (Validation AP metric)

INSTANCE SEGMENTATION KEYPOINT DETECTION OBJECT DETECTION VIDEO CLASSIFICATION

Feature Pyramid Networks for Object Detection

CVPR 2017 facebookresearch/detectron

Feature pyramids are a basic component in recognition systems for detecting objects at different scales.

OBJECT DETECTION