2D object detection
55 papers with code • 7 benchmarks • 32 datasets
These leaderboards are used to track progress in 2D object detection
LibrariesUse these libraries to find 2D object detection models and implementations
Most implemented papers
Scaled-YOLOv4: Scaling Cross Stage Partial Network
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.
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
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.
PP-YOLOE: An evolved version of YOLO
In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment.
Small Object Detection via Pixel Level Balancing With Applications to Blood Cell Detection
This method can perform well with blood cell detection in our experiments.
DETRs Beat YOLOs on Real-time Object Detection
In this paper, we first analyze the influence of NMS in modern real-time object detectors on inference speed, and establish an end-to-end speed benchmark.
Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes
The task of detecting 3D objects in traffic scenes has a pivotal role in many real-world applications.
EfficientPose: An efficient, accurate and scalable end-to-end 6D multi object pose estimation approach
Through the inherent handling of multiple objects and instances and the fused single shot 2D object detection as well as 6D pose estimation, our approach runs even with multiple objects (eight) end-to-end at over 26 FPS, making it highly attractive to many real world scenarios.
Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud
We propose an attentive module to fit the sparse feature maps to dense mostly on the object regions through the deformable convolution tower and the supervised mask-guided attention.
RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection
In particular, we propose to generate random layouts of a scene by making use of the objects in the synthetic CAD dataset and learn the 3D scene representation by applying object-level contrastive learning on two random scenes generated from the same set of synthetic objects.
Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection
To enhance object detection in a dark environment, we propose a novel multitask auto encoding transformation (MAET) model which is able to explore the intrinsic pattern behind illumination translation.