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/laycoding/PostiveDet

Single-Shot Refinement Neural Network for Object Detection

https://github.com/laycoding/renlianjiance

Single-Shot Refinement Neural Network for Object Detection

https://github.com/layumi/Person_reID_baseline_pytorch

Random Erasing Data Augmentation

https://github.com/layumi/University1652-Baseline

Random Erasing Data Augmentation

https://github.com/lazyn1997/YOLOX

YOLOX: Exceeding YOLO Series in 2021

https://github.com/lbareiro/Image_Segmentation-master

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/lccol/vig-eo

Vision GNN: An Image is Worth Graph of Nodes

https://github.com/lcxzzz0113/CenterNet

Objects as Points

https://github.com/lcybuzz/Low-Level-Vision-Paper-Record

Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

https://github.com/leaderj1001/Attention-Augmented-Conv2d

Attention Augmented Convolutional Networks

https://github.com/leaderj1001/BottleneckTransformers

Bottleneck Transformers for Visual Recognition

https://github.com/leaderj1001/LambdaNetworks

LambdaNetworks: Modeling Long-Range Interactions Without Attention

https://github.com/leaderj1001/Stand-Alone-Self-Attention

Attention Augmented Convolutional Networks

https://github.com/leanderme/ConvNeXt-Tensorflow

A ConvNet for the 2020s

https://github.com/leaplabthu/efficienttrain

Masked Autoencoders Are Scalable Vision Learners

https://github.com/leaplabthu/laudnet

Latency-aware Spatial-wise Dynamic Networks

https://github.com/leduckhai/Global-Wheat-Detection-Kaggle

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/lee-man/movenet

Objects as Points

https://github.com/lee-zq/VesselSeg-Pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/leeBooMla/ICSR

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

https://github.com/leeboomla/calr

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

https://github.com/leeh43/Singularity_Deeplesion

MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation

https://github.com/leejang/two_stream_ssd_caffe

SSD: Single Shot MultiBox Detector

https://github.com/leemathew1998/GradientWeight

Deep Residual Learning for Image Recognition

https://github.com/leemathew1998/RG

Deep Residual Learning for Image Recognition

https://github.com/leemengwei/my_mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/leeyegy/SimDR

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/leeyegy/simcc

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/leggedrobotics/darknet

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/leggedrobotics/darknet_ros

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

https://github.com/leggedrobotics/raw_image_pipeline

Convolutional Color Constancy

https://github.com/leimao/DeepLab-V3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/leimao/DeepLab_v3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/lemonadec/Relevance-between-Accuracy-under-Black-box-Attack-and-the-Similarity-between-Networks

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/leoandeol/cods

Towards PAC Multi-Object Detection and Tracking

https://github.com/leobean/CenterNet_oriented

Objects as Points

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