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/AbhinavJhanwar/object-detection-classification

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

https://github.com/Abhisek-/AlexNet

ImageNet Large Scale Visual Recognition Challenge

https://github.com/AceCoooool/DSS-pytorch

Deeply supervised salient object detection with short connections

https://github.com/AceCoooool/NLDF-pytorch

https://github.com/Adam123wu/Edge-YOLO_algorithm

YOLOv3: An Incremental Improvement

https://github.com/Adarol/u-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/AdeelH/pytorch-multi-class-focal-loss

Focal Loss for Dense Object Detection

https://github.com/Adelaide-AI-Group/FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Adityarajora/qwerty

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Adlith/tph-yolov5

TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios

https://github.com/AdolfoMatias/rede_neural_laranja_maca_adolfo

YOLOv3: An Incremental Improvement

https://github.com/Adren98/EczemaApp

Scalable Object Detection using Deep Neural Networks

https://github.com/Adrian398/CB-Net-EDD

CBNet: A Novel Composite Backbone Network Architecture for Object Detection

https://github.com/AemikaChow/DATASOURCE

ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations

https://github.com/Aggrawal5497/UNet-Image-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Ahmad-Omar-Ahsan/CycleMLP

CycleMLP: A MLP-like Architecture for Dense Prediction

https://github.com/AhrazA/modelcompression-2019

Deep Neural Network Compression for Image Classification and Object Detection

https://github.com/Ahsanr312/Object-Detection-and-Tracking-using-YOLOv3-and-DeepSort

YOLOv3: An Incremental Improvement

https://github.com/Ahsanr312/Object-Detection-in-Satellite-Imagery-Using-YOLOv3-

YOLOv3: An Incremental Improvement

https://github.com/AiLvv/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/AishrithRao/robust-physical-attack

ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector

https://github.com/AjJordy/squeezeDet-master

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/AkashGanesan/PedestrianAttention

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/Akhilesh64/Image-Segmentation-U-2-Net

U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection

https://github.com/AlanFokCo/compensation-tools

Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources

https://github.com/AlassaneSakande/A-ConvNet-of-2020s

A ConvNet for the 2020s

https://github.com/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection

Robust and Efficient Post-Processing for Video Object Detection (REPP)

https://github.com/Alexander-Whelan/Zeus

Mask R-CNN

https://github.com/AlexandreBrown/AerialSemanticSegmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/AlexandreMahdhaoui/fast-layers

Going Deeper with Convolutions

https://github.com/AlexandreMahdhaoui/fast_layers

Going Deeper with Convolutions

https://github.com/Alibaba-MIIL/TResNet

TResNet: High Performance GPU-Dedicated Architecture

https://github.com/AllenPeng0209/SaccadeNet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/AllenPeng0209/deeproute_competition

Exploring Data Augmentation for Multi-Modality 3D Object Detection

https://github.com/AlongRide/CenterNet_anchor_free

Objects as Points

https://github.com/AlphaGoMK/arxiv-explorer

3D Object Detection From LiDAR Data Using Distance Dependent Feature Extraction

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