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/TranThienDat-Nguyen/VisionEvaluation

How Trustworthy are Performance Evaluations for Basic Vision Tasks?

https://github.com/TravisLeeTS/grabcvchallenge

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/TravisTorch/Pytorch

YOLOv3: An Incremental Improvement

https://github.com/TristansCloud/YellowstonesVegitiation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Tsejing/object_detection

SSD: Single Shot MultiBox Detector

https://github.com/Tu-kun/cultivated_landdivision

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/TuSimple/RangeDet

Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection

https://github.com/TuSimple/mx-maskrcnn

Mask R-CNN

https://github.com/TuSimple/neuron-selectivity-transfer

Like What You Like: Knowledge Distill via Neuron Selectivity Transfer

https://github.com/TugberkErol/Semantic-Segmentation-/blob/main/unet.py

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/TurtleGo/project-vehicle-detect

SSD: Single Shot MultiBox Detector

https://github.com/TusharMhaske28/Object-Detection

YOLOv3: An Incremental Improvement

https://github.com/TusharMhaske28/Thermal-Object

YOLOv3: An Incremental Improvement

https://github.com/TusharMhaske28/summer-project

YOLOv3: An Incremental Improvement

https://github.com/UAws/CV-3315-Is-All-You-Need

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/UBC-CIC/COVID19-L3-Net

Pyramid Scene Parsing Network

https://github.com/UESTC-Liuxin/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/UPCLJ/py-faster-rcnn

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

https://github.com/USTC-IMCL/HST-for-Compressed-SR

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/USTCzzl/Deecamp27

Bi-Directional Cascade Network for Perceptual Edge Detection

https://github.com/Ugenteraan/Masked-AutoEncoder-PyTorch

Masked Autoencoders Are Scalable Vision Learners

https://github.com/Ugenteraan/YOLOv1

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

https://github.com/Ugness/PiCANet-Implementation

PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection

https://github.com/Unity-Technologies/SynthDet

An Annotation Saved is an Annotation Earned: Using Fully Synthetic Training for Object Instance Detection

https://github.com/UnofficialJuliaMirror/DeepMark-deepmark

Deep Residual Learning for Image Recognition

https://github.com/UnofficialJuliaMirrorSnapshots/DeepMark-deepmark

Deep Residual Learning for Image Recognition

https://github.com/Uranium-Deng/Steganalysis-StegoRemoval

CBAM: Convolutional Block Attention Module

https://github.com/UsmannK/cloud-mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/UtkarshKharche29/Automatic-Number-Plate-Recognition

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Uuuuuuho/Pillar_on_SoC

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/V3Det/Detectron2-V3Det

V3Det: Vast Vocabulary Visual Detection Dataset

https://github.com/V3Det/mmdetection-V3Det

RTMDet: An Empirical Study of Designing Real-Time Object Detectors

https://github.com/VCIP-RGBD/DFormer

DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation

https://github.com/VCasecnikovs/Yet-Another-YOLOv4-Pytorch

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/VLTSeg/VLTSeg

Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer Learning

https://github.com/VaanHUANG/CSCI5210HW1

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

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