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/hysshy/hyv9_mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/hysts/pytorch_D-RISE

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/hysts/pytorch_resnet

Deep Residual Learning for Image Recognition

https://github.com/hysts/pytorch_resnet_preact

Deep Residual Learning for Image Recognition

https://github.com/hyunjoonbok/Unet-pytorch

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/hz-ants/DenseFusion

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

https://github.com/hz-ants/Pose2Seg

Pose2Seg: Detection Free Human Instance Segmentation

https://github.com/hz-ants/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/hz2538/iot_JennyGo

Deep Residual Learning for Image Recognition

https://github.com/hzlbbfrog/generative-bim

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/iArunava/ResNet

Deep Residual Learning for Image Recognition

https://github.com/iacercalixto/butd-image-captioning

Are scene graphs good enough to improve Image Captioning?

https://github.com/iamilyasedunov/key_word_spotting

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/iamsingh/darknet

YOLOv3: An Incremental Improvement

https://github.com/iantimmis/DropBlock-Keras-Implementation

DropBlock: A regularization method for convolutional networks

https://github.com/iarai/landslide4sense-2022

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/iarai/weather4cast

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ibaiGorordo/ONNX-HybridNets-Multitask-Road-Detection

HybridNets: End-to-End Perception Network

https://github.com/ibaiGorordo/ONNX-Object-Localization-Network

Learning Open-World Object Proposals without Learning to Classify

https://github.com/ibaiGorordo/ONNX-YOLO-World-Open-Vocabulary-Object-Detection

YOLO-World: Real-Time Open-Vocabulary Object Detection

https://github.com/ibaiGorordo/ONNX-YOLOv10-Object-Detection

YOLOv10: Real-Time End-to-End Object Detection

https://github.com/ibaiGorordo/ONNX-YOLOv7-Object-Detection

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

https://github.com/ibaiGorordo/ONNX-YOLOv9-MIT-Object-Detection

YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

https://github.com/iberganzo/darknet

YOLOv3: An Incremental Improvement

https://github.com/ibm/max-human-pose-estimator

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/ibrahiemhss/android-yolo

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

https://github.com/ibrahimethemhamamci/dentex

Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays

https://github.com/ice-melt/csdn-w9

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/icyeyeball/Peitent

Object Detection for Comics using Manga109 Annotations

https://github.com/idea-research/dab-detr

DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

https://github.com/idea-research/dino

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

https://github.com/idea-research/grounded-sam-2

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

https://github.com/idea-research/stable-dino

A Strong and Reproducible Object Detector with Only Public Datasets

https://github.com/idea-research/stabledino

A Strong and Reproducible Object Detector with Only Public Datasets

https://github.com/idealo/imagededup

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/iduta/pyconvsegnet

Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition

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