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/dreamway/CenterNet-objects-as-points

Objects as Points

https://github.com/dronefreak/human-action-classification

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/dry01/segmentation-

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ds4sd/quackling

DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis

https://github.com/dsandeep0138/UNet-Colorization

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/dsba-lab/openal

Learning Loss for Active Learning

https://github.com/dslisleedh/MetaFormer-flax

MetaFormer Is Actually What You Need for Vision

https://github.com/dssbxx/k_labels_resnet50

Deep Residual Learning for Image Recognition

https://github.com/dtthi/darknetAlexeyAB

YOLO9000: Better, Faster, Stronger

https://github.com/duchaba/Norwegian_Blue_Parrot_k2fa_AI

Deep Residual Learning for Image Recognition

https://github.com/ducminhkhoi/FAPIS

One-Shot Instance Segmentation

https://github.com/ducongju/HRNet

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/dugusiqing/caffe

SSD: Single Shot MultiBox Detector

https://github.com/duongnv0499/Explain-Deformable-DETR

Deformable DETR: Deformable Transformers for End-to-End Object Detection

https://github.com/durandtibo/wildcat.pytorch

https://github.com/dvl-tum/motsynth-baselines

Mask R-CNN

https://github.com/dvlab-research/3d-box-segment-anything

VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking

https://github.com/dvlab-research/mask-attention-free-transformer

Spherical Transformer for LiDAR-based 3D Recognition

https://github.com/dvlab-research/rescom

Vision GNN: An Image is Worth Graph of Nodes

https://github.com/dvlab-research/reviewkd

Distilling Knowledge via Knowledge Review

https://github.com/dvndra/car_detection_yolo

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

https://github.com/dvornikita/blitznet

BlitzNet: A Real-Time Deep Network for Scene Understanding

https://github.com/dvornikita/context_aug

Modeling Visual Context is Key to Augmenting Object Detection Datasets

https://github.com/dwaithe/darknet3AB

YOLO9000: Better, Faster, Stronger

https://github.com/dwaithe/yolov2

YOLO9000: Better, Faster, Stronger

https://github.com/dwtstore/sfm1

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/dyabel/wsod-mmdet

Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection

https://github.com/dyadav4/Autonomous-driving-application-car-detection

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

https://github.com/dylanamiller/non_max_suppression

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

https://github.com/dzhaoxd/seco

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/e-lab/linknet

LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation

https://github.com/e-lab/pytorch-linknet

LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation

https://github.com/e0015274/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/e9190712/vessel_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/e96031413/AA-YOLO

Squeeze-and-Excitation Networks

https://github.com/e96031413/PyTorch_YOLOv4-tiny

Squeeze-and-Excitation Networks

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