2D Object Detection
84 papers with code • 14 benchmarks • 57 datasets
Benchmarks
These leaderboards are used to track progress in 2D Object Detection
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Libraries
Use these libraries to find 2D Object Detection models and implementationsDatasets
BDD100K
VisDrone
Hypersim
COCO-O
ExDark
Clear Weather
KITTI MOTS
FlickrLogos-32
RADIATE
PACO
Subtasks
Most implemented papers
Focal Loss for Dense Object Detection
facebookresearch/detectron
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ICCV 2017
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.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
rbgirshick/py-faster-rcnn
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NeurIPS 2015
In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals.
FCOS: Fully Convolutional One-Stage Object Detection
tianzhi0549/FCOS
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ICCV 2019
By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training.
Scaled-YOLOv4: Scaling Cross Stage Partial Network
WongKinYiu/ScaledYOLOv4
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CVPR 2021
We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy.
YOLOX: Exceeding YOLO Series in 2021
Megvii-BaseDetection/YOLOX
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In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX.
End-to-End Object Detection with Transformers
facebookresearch/detr
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ECCV 2020
We present a new method that views object detection as a direct set prediction problem.
Deformable DETR: Deformable Transformers for End-to-End Object Detection
fundamentalvision/Deformable-DETR
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ICLR 2021
DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance.
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
wongkinyiu/yolov7
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CVPR 2023
YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56. 8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100.
Cascade R-CNN: Delving into High Quality Object Detection
zhaoweicai/cascade-rcnn
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CVPR 2018
In object detection, an intersection over union (IoU) threshold is required to define positives and negatives.
PP-YOLOE: An evolved version of YOLO
PaddlePaddle/PaddleDetection
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In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment.
