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/irmvlab/point-mamba

OctFormer: Octree-based Transformers for 3D Point Clouds

https://github.com/irvankadhafi/Burnt-PCB-Object-Detection

EfficientDet: Scalable and Efficient Object Detection

https://github.com/isaaccorley/hydro-foundation-model

Masked Autoencoders Are Scalable Vision Learners

https://github.com/isaachaw/GrabCarRecognition

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/isakdiaz/PlantNet_MobileNetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/isbrycee/gem-glass-segmentor

Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation

https://github.com/iset/iset3d

Soft Prototyping Camera Designs for Car Detection Based on a Convolutional Neural Network

https://github.com/isheunesutembo/TB-Computer-Aided-Diagnosis-Using-Deep-Learning

Deep Residual Learning for Image Recognition

https://github.com/iskandari/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/isl-org/Open3D-ML

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

https://github.com/islamosmanubc/MedMAE

Masked Autoencoders Are Scalable Vision Learners

https://github.com/issaiass/FacialMaskDetector

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

https://github.com/istvanmegyeri/audio_tagging

Focal Loss for Dense Object Detection

https://github.com/it6aidl/outdoorsegmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ithuanhuan/gpu-py3-faster-rcnn

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

https://github.com/ithuanhuan/py-fatser-rcnn

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

https://github.com/itijyou/ademxapp

Deep Residual Learning for Image Recognition

https://github.com/itsasimiqbal/SeBRe

Mask R-CNN

https://github.com/ivanwhaf/yolov1-pytorch

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

https://github.com/ivanwilliammd/I3DR-Net-Transfer-Learning

Focal Loss for Dense Object Detection

https://github.com/ivboh/3d_image_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ixiaohuihuihui/ao2-detr

AO2-DETR: Arbitrary-Oriented Object Detection Transformer

https://github.com/j3soon/arxiv-utils

Deep Residual Learning for Image Recognition

https://github.com/j96w/DenseFusion

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

https://github.com/jack16888/caffe-ssd

SSD: Single Shot MultiBox Detector

https://github.com/jack16888/caffessd

SSD: Single Shot MultiBox Detector

https://github.com/jacksonlli/ProjectQuarantine_HumanSegmentation

Pose2Seg: Detection Free Human Instance Segmentation

https://github.com/jacobyuan7/rlip

RLIPv2: Fast Scaling of Relational Language-Image Pre-training

https://github.com/jaehyunnn/LambdaNetworks_pytorch

LambdaNetworks: Modeling Long-Range Interactions Without Attention

https://github.com/jahongir7174/EfficientDet-tf

EfficientDet: Scalable and Efficient Object Detection

https://github.com/jahongir7174/EfficientNetV2

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/jahongir7174/MaskRCNN

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/jahongir7174/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/jahutwb/DL_dosimetry

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

https://github.com/jainabhishek/mnetv2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/jaiwei98/mobile-vit-pytorch

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

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