Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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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/Raghvender1205/ConvNeXt

A ConvNet for the 2020s

https://github.com/Rahmanzia3/darknet

YOLOv3: An Incremental Improvement

https://github.com/Rahmanzia3/darknet_original

YOLOv3: An Incremental Improvement

https://github.com/Rahmanzia3/yolo

YOLOv3: An Incremental Improvement

https://github.com/RainbowNebula/PETS

Periodically Exchange Teacher-Student for Source-Free Object Detection

https://github.com/RaiyaniNirav/Mask-R-CNN-for-water-detection

Mask R-CNN

https://github.com/RajArPatra/Super-OCR

Mask R-CNN

https://github.com/Rakeshvd/Instance-Segmenation-of-MRI-scan-using-Mask-RCNN

Mask R-CNN

https://github.com/Rakeshvd/Semantic-Segmenation-of-MRI-scan-using-Mask-RCNN

Mask R-CNN

https://github.com/Rakshit-Shetty/Resnet-Implementation

Deep Residual Learning for Image Recognition

https://github.com/RamposhPidrov/waifu2face

Searching for MobileNetV3

https://github.com/Randl/MobileNetV3-pytorch

Searching for MobileNetV3

https://github.com/RangiLyu/mmdetection_test

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/RangiLyu/nanodet

Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

https://github.com/Ranja-S/sensitivity

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

https://github.com/RashadGarayev/OpencvDnnmodule

SSD: Single Shot MultiBox Detector

https://github.com/RayXie29/SENet_Keras

Squeeze-and-Excitation Networks

https://github.com/Raychou0830/Carplate

YOLOv3: An Incremental Improvement

https://github.com/RealHulubulu/openCVObjectDetectionRPi

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection

On Complex Valued Convolutional Neural Networks

https://github.com/Recognito-Vision/Face-SDK-Linux-Demos

Continual Forgetting for Pre-trained Vision Models

https://github.com/RedWang1989/YOLOv4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Reemr/Cancer-detection

Detecting Cancer Metastases on Gigapixel Pathology Images

https://github.com/RentadroneCL/Photovoltaic_Fault_Detector

SSD: Single Shot MultiBox Detector

https://github.com/RentadroneCL/model-definition

SSD: Single Shot MultiBox Detector

https://github.com/Res2Net/Res2Net-PoolNet

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/Res2Net/Res2Net-Pose-Estimation

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/Res2Net/Res2Net-PretrainedModels

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/Res2Net/Res2Net-maskrcnn

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/Res2Net/mmdetection

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/Resham-Sundar/sahi-yolox

Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection

https://github.com/Richard-cpu2333/tx2dl

SSD: Single Shot MultiBox Detector

https://github.com/RichardMathewsII/YOLBO

Focal Loss for Dense Object Detection

https://github.com/RichardMinsooGo-ML/Pytorch-Complex-Yolo-Yolov3

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/RichardMinsooGo-ML/Pytorch-Complex-Yolo-Yolov4

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov3

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

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