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/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/NatiOfir/DeepFaintEdges

Multi-scale Processing of Noisy Images using Edge Preservation Losses

https://github.com/NatiOfir/FaintCurvedEdgeDetection

Fast Detection of Curved Edges at Low SNR

https://github.com/NatiOfir/FaintStraightEdgeDetection

On Detection of Faint Edges in Noisy Images

https://github.com/NatiOfir/FastEdgesCpp

Fast Detection of Curved Edges at Low SNR

https://github.com/NatiOfir/TrianglesEdgeDetection

Fast Detection of Curved Edges at Low SNR

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

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/NeuronDroid/GVSS-S.A.Drone

SegNet: A Deep Convolutional Encoder-Decoder Architecture for 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

https://github.com/Niloy-Chakraborty/Image_Segmentation_Model

Fully Convolutional Networks for Semantic Segmentation

https://github.com/Nioolek/PPYOLOE_pytorch

PP-YOLOE: An evolved version of YOLO

https://github.com/NiravRaiyani/Mask-R-CNN-for-water-detection

Mask R-CNN

https://github.com/Nithesh5/torch-adni-bagging-with-attentional

CBAM: Convolutional Block Attention Module

https://github.com/Nithesh5/torch-adni-bootstrap-with-attentional

CBAM: Convolutional Block Attention Module

https://github.com/NonMagneticNeedle/vision.ai

Objects as Points

https://github.com/NovasMax/R3Det-Refined-Single-Stage-Detector-with-Feature-Refinement-for-RO

Focal Loss for Dense Object Detection

https://github.com/NovasMax/yolov3

YOLOv3: An Incremental Improvement

https://github.com/Nullius-2020/MAE-Paddle

Masked Autoencoders Are Scalable Vision Learners

https://github.com/NumericalAdvantage/ld-node-facedetector-2

Max-Margin Object Detection

https://github.com/OFRIN/Tensorflow_FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/OFRIN/Tensorflow_GIoU

Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

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