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/MrRiahi/Convolutional-Neural-Networks-Tensorflow

Going Deeper with Convolutions

https://github.com/Mrnoorsingh/car-parking

Mask R-CNN

https://github.com/Mrqianduoduo/RSDet-8P-4R

Learning Modulated Loss for Rotated Object Detection

https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020

Deformable Convolutional Networks

https://github.com/Muthu2093/U-Net-for-Brain-Tumor-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Myyyr/transseg2d

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/NANSHANB/StereoR-CNN

Stereo R-CNN based 3D Object Detection for Autonomous Driving

https://github.com/NASA-NeMO-Net/NeMO-Net

Deep Residual Learning for Image Recognition

https://github.com/NBvinay/vinb-ghost-car

YOLOv3: An Incremental Improvement

https://github.com/NCNU-OpenSource/What-are-you-

SSD: Single Shot MultiBox Detector

https://github.com/NERSC/sc21-dl-tutorial

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/NEUAutonomousDriving408/CIVCAutonomousDriving

YOLOX: Exceeding YOLO Series in 2021

https://github.com/NEUdeep/Swin-Transformer-Object-Detection

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/NU-LL/lighttrack-

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/NVIDIA/DeepLearningExamples

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/NVIDIA/MinkowskiEngine

Generative Sparse Detection Networks for 3D Single-shot Object Detection

https://github.com/NVIDIA/retinanet-examples

Deep Residual Learning for Image Recognition

https://github.com/NVIDIAAICITYCHALLENGE/AICity_Team6_ISU

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/NVlabs/DG-Net

Random Erasing Data Augmentation

https://github.com/NVlabs/PAMTRI

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/NVlabs/STL

Understanding The Robustness in Vision Transformers

https://github.com/NVlabs/industreallib

Mask R-CNN

https://github.com/NYU-DICE-Lab/open_clip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/NZ99/bottleneck-transformer-flax

Bottleneck Transformers for Visual Recognition

https://github.com/NaCl-Ocean/Anchor_free_detection_rotation

1st Place Solutions of Waymo Open Dataset Challenge 2020 -- 2D Object Detection Track

https://github.com/Nacriema/Loss-Functions-For-Semantic-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Nandu960/Road-Asset-Detection

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

https://github.com/NaturalHistoryMuseum/semantic-segmentation

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Nguyendat-bit/MobilenetV1

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Nguyendat-bit/MobilenetV2

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Nguyendat-bit/U-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Nicholasli1995/EgoNet

M3D-RPN: Monocular 3D Region Proposal Network for Object Detection

https://github.com/NicolaBernini/PaperAnalysis_ICLR2021_NegativeDataAugmentation

Negative Data Augmentation

https://github.com/NicoleEic/Brainhack_Oxbridge

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Nikhil619/Autonomous-driving-with-camera-using-Yolo

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

https://github.com/Nikhil619/Motion-direction-control-of-a-pi-robot-using-hand-movementss

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

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