Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

39

평가표

124

보존된 평가 기록

977

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/KookHoiKim/GroupFree3dBaseline

Group-Free 3D Object Detection via Transformers

https://github.com/KostadinovShalon/oln-mask

Learning Open-World Object Proposals without Learning to Classify

https://github.com/Krupal09/MaskRCNN-Demo

Mask R-CNN

https://github.com/Kungbohan/EECSMed

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/KushaalShroff/Signature-and-Annotation-detection

Fast R-CNN

https://github.com/KwangHoonAn/Quantizations

Data-Free Quantization Through Weight Equalization and Bias Correction

https://github.com/KyeongmoonKim/sb

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/L-Reichardt/Cylinder3D-updated-CUDA

Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation

https://github.com/LEGO999/YOLOV3-TF2

YOLOv3: An Incremental Improvement

https://github.com/LEOCUIZHIHAO/kpmask

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/LIU1514/Yolov3-

YOLO9000: Better, Faster, Stronger

https://github.com/LKLQQ/CBAM

CBAM: Convolutional Block Attention Module

https://github.com/LKLQQ/HireMLP

Hire-MLP: Vision MLP via Hierarchical Rearrangement

https://github.com/LKLQQ/detr

End-to-End Object Detection with Transformers

https://github.com/LKLQQ/pointpillars

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/LKLQQ/ssc_resnet50

Deep Residual Learning for Image Recognition

https://github.com/LKLQQ/tracktor

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/LONG-9621/Extract_Point_3D

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/LONG-9621/PointNet

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/LONG-9621/PointNet-

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/LONG-9621/VoteNet

Deep Hough Voting for 3D Object Detection in Point Clouds

https://github.com/LRacoci/permutation-graphml

Non-local Neural Networks

https://github.com/LTTM/IL-SemSegm

Incremental Learning Techniques for Semantic Segmentation

https://github.com/Lakerszjb/CentereNet-C

Objects as Points

https://github.com/LanqingLi/Chromosome

SSD: Single Shot MultiBox Detector

https://github.com/LarryJiang134/Image_manipulation_detection

Learning Rich Features for Image Manipulation Detection

https://github.com/Launch-on-Titania/DynOPETs

DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera Scenarios

https://github.com/Lavender105/DFF

CASENet: Deep Category-Aware Semantic Edge Detection

https://github.com/LdDl/go-darknet

YOLOv3: An Incremental Improvement

https://github.com/LdDl/odam

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/LdDl/rust-road-traffic

YOLOv3: An Incremental Improvement

https://github.com/LeHoaiLinh278/mmdetection_dev

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Lebhoryi/keras-YOLOv3-model-set

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/LebronGG/PointNet

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/Lechatelia/detection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/LeeDongYeun/keras-m2det

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.