Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

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

https://github.com/binhdv92/darknet_kriyeng

YOLO9000: Better, Faster, Stronger

https://github.com/birosjh/pytorch_ssd

SSD: Single Shot MultiBox Detector

https://github.com/birosjh/simple_ssd

SSD: Single Shot MultiBox Detector

https://github.com/biss/unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/bkvie/Locally-Consistent-Deformable-Convolution

Deformable Convolutional Networks

https://github.com/blake58/openpose

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/blankWorld/GroupNorm-caffe

Group Normalization

https://github.com/blaze1522/Image-Segmentation-FloodNet

FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding

https://github.com/bleedingfight/caffe-env

SSD: Single Shot MultiBox Detector

https://github.com/blueardour/AdelaiDet

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/bluecoderbot/hed-master

Holistically-Nested Edge Detection

https://github.com/blzzrd/ComputerVisionFinal

Background Image Generation Using Boolean Operations

https://github.com/bmoradi93/ssd-object-detection

SSD: Single Shot MultiBox Detector

https://github.com/bmstu-iu8-g1-2019-project/road-signs-recognition

Fast R-CNN

https://github.com/bo-10000/VoxResNet

VoxResNet: Deep Voxelwise Residual Networks for Volumetric Brain Segmentation

https://github.com/bobby20180331/darknet_pycharm

YOLO9000: Better, Faster, Stronger

https://github.com/bok9504/UAV_MOT_LineRegistration

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/bolianchen/nuscenes_depth

nuScenes: A multimodal dataset for autonomous driving

https://github.com/bonlime/keras-deeplab-v3-plus

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/boom85423/Cartoon-style-Stickers-Generator

Mask R-CNN

https://github.com/boom85423/Seq2seq-model-for-Meme-Generator

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

https://github.com/boris127/vehicle-detection

Deep Residual Learning for Image Recognition

https://github.com/borisdayma/clip-jax

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/bostondiditeam/MV3D

Multi-View 3D Object Detection Network for Autonomous Driving

https://github.com/bowensong0312/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/bowu1004/instance_segmentation_RealSense

Mask R-CNN

https://github.com/boyellow/adaad

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/bpuladi/Automated-PD-L1-Scoring

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/bradenhurl/DeepGTAV-PreSIL

Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception

https://github.com/bradenhurl/GTAVisionExport-DepthExtractor

Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception

https://github.com/bradenhurl/PreSIL-tools

Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception

https://github.com/bragancas/vnet_pytorch-atriaseg2018

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/branislavhesko/segmentation_framework

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/brbzjl/pointnet2

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

https://github.com/brdiep113/UNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/brightyoun/TITS-LPST

Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs

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