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/Pritam-N/ParNet

Non-deep Networks

https://github.com/ProhetTeam/QuantMmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/PuchatekwSzortach/face_detection

Multi-view Face Detection Using Deep Convolutional Neural Networks

https://github.com/PuchatekwSzortach/voc_ssd

SSD: Single Shot MultiBox Detector

https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation

Deep Residual Learning for Image Recognition

https://github.com/PurabPatel555/Pytorch-UNet-Brain-MRI

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Purkialo/CrowdDet

Detection in Crowded Scenes: One Proposal, Multiple Predictions

https://github.com/Pxtri2156/AdelaiDet_v2

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/PythonZhengbinChen/tf-faster-rcnn-cpu

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

https://github.com/Qdriving/Swin-TransformerV2-in-PP

Swin Transformer V2: Scaling Up Capacity and Resolution

https://github.com/Qengineering/MobileNetV1_SSD_OpenCV_Caffe

Speed/accuracy trade-offs for modern convolutional object detectors

https://github.com/Qengineering/MobileNetV2_YOLOV3_ncnn

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

https://github.com/Qengineering/MobileNet_SSD_OpenCV_TensorFlow

Speed/accuracy trade-offs for modern convolutional object detectors

https://github.com/Qengineering/NanoDet-ncnn-Jetson-Nano

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

https://github.com/Qengineering/NanoDet-ncnn-Raspberry-Pi-4

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

https://github.com/Qengineering/PeleeNet_SSD

Pelee: A Real-Time Object Detection System on Mobile Devices

https://github.com/Qengineering/Rfcn_ncnn

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

https://github.com/Qengineering/ShuffleNetV2-ncnn

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

https://github.com/Qengineering/TensorFlow_Lite_RPi_32-bits

Speed/accuracy trade-offs for modern convolutional object detectors

https://github.com/Qengineering/TensorFlow_Lite_RPi_64-bits

Speed/accuracy trade-offs for modern convolutional object detectors

https://github.com/Qengineering/TensorFlow_Lite_SSD_Jetson-Nano

Speed/accuracy trade-offs for modern convolutional object detectors

https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_32-bits

Speed/accuracy trade-offs for modern convolutional object detectors

https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_64-bits

Speed/accuracy trade-offs for modern convolutional object detectors

https://github.com/Qengineering/YoloV2-ncnn-Jetson-Nano

YOLO9000: Better, Faster, Stronger

https://github.com/Qengineering/YoloV2-ncnn-Raspberry-Pi-4

YOLO9000: Better, Faster, Stronger

https://github.com/Qengineering/YoloV3-ncnn-Jetson-Nano

YOLOv3: An Incremental Improvement

https://github.com/Qengineering/YoloV3-ncnn-Raspberry-Pi-4

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

https://github.com/Qengineering/YoloV4-ncnn-Jetson-Nano

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-64-bit

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/QiaoranC/tf_ResNeSt_RegNet_model

CBNet: A Novel Composite Backbone Network Architecture for Object Detection

https://github.com/Qinhj07/ATOMCode

Fast R-CNN

https://github.com/RACHANAMOVVA09/CenterNet

Objects as Points

https://github.com/RPFey/frustum-pointnets

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/RPraneetha/MoCo-FAIR

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/Radar-Camera-Fusion/Awesome-Radar-Perception

Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review

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