Papers with Code 아카이브

Object Detection

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하위 태스크

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평가표

124

보존된 평가 기록

977

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/stevensmiley1989/MrRobot

SSD: Single Shot MultiBox Detector

https://github.com/stevenzhou2017/MaskYolo

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

https://github.com/stevenzhou2017/darknet_AlexeyAB

YOLO9000: Better, Faster, Stronger

https://github.com/stevetsui/rebnn

Recurrent Bilinear Optimization for Binary Neural Networks

https://github.com/stevewongv/dsc-pytorch

Direction-aware Spatial Context Features for Shadow Detection

https://github.com/sthalles/deeplab_v3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/stigma0617/VoVNet-DeepLabV3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/stigma0617/VoVNet.pytorch

An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection

https://github.com/stigma0617/maskrcnn-benchmark-vovnet

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

https://github.com/stomachcold/hctransformers

ImageNet Large Scale Visual Recognition Challenge

https://github.com/strivebo/image_segmentation_dl

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/stupidZZ/pyc_repo

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

https://github.com/sty16/cell_mmdetection

Instances as Queries

https://github.com/subodh-malgonde/vehicle-detection

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

https://github.com/sudeep0503/DD-Net-Double-Domain-Guided-Real-Time-Low-Light-Image-Enhancement

Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance

https://github.com/sudharavali/objectDetectionYOLO

YOLO9000: Better, Faster, Stronger

https://github.com/sudharshan2001/PCB-defect-detection-using-U-NET

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/sufeidechabei/gluon-mobilenet-yolov3

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/suhas1999/Flip-kart-grid-challenge

Deep Residual Learning for Image Recognition

https://github.com/suikei-wang/facial-attributes-classification

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/suilin0432/mmdetectionWithCodeAnnotations

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/sujaygarlanka/basketball-shot-count

YOLOv3: An Incremental Improvement

https://github.com/sujaygarlanka/basketball_shot_count

YOLOv3: An Incremental Improvement

https://github.com/sulasen/race-events-recognition-1

Focal Loss for Dense Object Detection

https://github.com/sumitkutty/Anti-Spoof-Face-Recognition

Deep Residual Learning for Image Recognition

https://github.com/sumiya-kuroda/EMsegmentation-mito

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/sung0471/pytorch-YOLOv4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/sunhui1234/haha

Fast R-CNN

https://github.com/sunlizhuang/YOLOv1-PaddlePaddle

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

https://github.com/sunnyahlawat1713/face-mask-detector-python

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/sunnysoni97/single_image_fl

Deep Residual Learning for Image Recognition

https://github.com/sunqiangxtcsun/SSD

SSD: Single Shot MultiBox Detector

https://github.com/sunqiangxtcsun/faster-rcnn

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

https://github.com/sunutf/TSM

Non-local Neural Networks

https://github.com/super-wcg/Relation-Network-for-Object-Detection

Relation Networks for Object Detection

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