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/Guanghan/ROLO

You Only Look Once: Unified, Real-Time Object Detection

https://github.com/Guanghan/mxnet-centernet

Objects as Points

https://github.com/GuangyanZhang/SCNN-Deeplabv3-bisenet-icnet

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

https://github.com/Gugan0905/steel-defect-detection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Guillem96/efficient-net-tf

Focal Loss for Dense Object Detection

https://github.com/Guillem96/efficientdet-tf

Focal Loss for Dense Object Detection

https://github.com/Guillem96/ssd-pytorch

SSD: Single Shot MultiBox Detector

https://github.com/GunjanChourasia/custom_detector_yolov3

YOLOv3: An Incremental Improvement

https://github.com/GuoLiuFang/maskrcnn-benchmark-lfs

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

https://github.com/GuoQuanhao/EfficientDet-Paddle

EfficientDet: Scalable and Efficient Object Detection

https://github.com/GuoQuanhao/ScaledYOLOv4-Paddle

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/GuoQuanhao/YOLOv4-Paddle

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Gus-Guo/AugFPN

AugFPN: Improving Multi-scale Feature Learning for Object Detection

https://github.com/HAN-ARK/GVSS-S.A.Drone

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/HAOCHENYE/yehc_mmdet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/HCPLab-SYSU/SR

Dual-Glance Model for Deciphering Social Relationships

https://github.com/HHTseng/video-classification

Deep Residual Learning for Image Recognition

https://github.com/HHY-ZJU/Unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/HO4X/TSR_JetsonTX2

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/HRNet/HRNet-FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/HRNet/HRNet-Facial-Landmark-Detection

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/HRNet/HRNet-Human-Pose-Estimation

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/HRNet/HRNet-Image-Classification

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/HRNet/HRNet-MaskRCNN-Benchmark

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/HRNet/HRNet-Object-Detection

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/HRNet/HRNet-Semantic-Segmentation

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/HSRW-EMRP-WS1920-Drone/object_detection_and_landing

YOLO9000: Better, Faster, Stronger

https://github.com/HSoo-Kim/SpatioTemporal-AutoEncoder

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder

https://github.com/HadiZayer/UNet_pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Hadisalman/robust-verify-benchmark

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/HaiqalLM10/YOLO-Vehicle-Counting

YOLO9000: Better, Faster, Stronger

https://github.com/Halesu/4th-ML100Days

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/HaloTrouvaille/YOLO-Multi-Backbones-Attention

Squeeze-and-Excitation Networks

https://github.com/HanYong201308/Pytorch-UNet2

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Hanson0910/RetinaTrack

RetinaTrack: Online Single Stage Joint Detection and Tracking

https://github.com/Hansxsourse/Cross-Modality-Knowledge-Distillation-for-Multi-modal-Aerial-View-Object-Classification

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