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/IMPLabUniPr/mmdetection-groie

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/IMvision12/keras-vision-models

Deep Residual Learning for Image Recognition

https://github.com/IPIU-detection/SEANet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/IPLI/IPLI_Server

YOLO9000: Better, Faster, Stronger

https://github.com/IRVLab/SUIM

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/IRVLab/SUIM-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/IRVLab/funie-gan

Fast Underwater Image Enhancement for Improved Visual Perception

https://github.com/Iamal1/maskrcnn-benchmark

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

https://github.com/IcarusWizard/MAE

Masked Autoencoders Are Scalable Vision Learners

https://github.com/IdiosyncraticDragon/relief_rcnn

Relief R-CNN : Utilizing Convolutional Features for Fast Object Detection

https://github.com/IgorMunizS/mobileDet

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/IgorSusmelj/barlowtwins

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/IgorZaton/Mini-Projects

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/IliasMAOUDJ/dataset_gen

Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection

https://github.com/IlliaOvcharenko/lung-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/IlyaDobrynin/GridMixup

GridMask Data Augmentation

https://github.com/IlyaOvodov/AngelinaReader

Focal Loss for Dense Object Detection

https://github.com/ImagingLab/Colorizing-with-GANs

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Information-Fusion-Lab-Umass/Salient-Pedestrian-Detection

Pedestrian Detection in Thermal Images using Saliency Maps

https://github.com/InquisitiveOjas/Object-Detection_SSD

SSD: Single Shot MultiBox Detector

https://github.com/Instinct323/wiou/blob/v2/iou.py

Focal and Efficient IOU Loss for Accurate Bounding Box Regression

https://github.com/Iqbal1282/YoloV3

Soft-NMS -- Improving Object Detection With One Line of Code

https://github.com/Iqbal1282/yolov3_incremental_distillation_loss

YOLOv3: An Incremental Improvement

https://github.com/IsHYuhi/ST-CGAN_Stacked_Conditional_Generative_Adversarial_Networks

Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

https://github.com/IsaacPatole/TGS-Salt-Identification-Challenge

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Ivan-LZY/SG-Cyclingscapes

The Cityscapes Dataset for Semantic Urban Scene Understanding

https://github.com/IyatomiLab/CE-CLCNN

Random Erasing Data Augmentation

https://github.com/J-jjj7230/u-net_model

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/JC1DA/yolo_streamer

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/JCamERG/YOLOPersonal

YOLOv3: An Incremental Improvement

https://github.com/JContro/pytorch_projects

Deep Residual Learning for Image Recognition

https://github.com/JDAI-CV/CoTNet

Contextual Transformer Networks for Visual Recognition

https://github.com/JDAI-CV/CoTNet-ObjectDetection-InstanceSegmentation

Contextual Transformer Networks for Visual Recognition

https://github.com/JDAI-CV/DCL

Look-into-Object: Self-supervised Structure Modeling for Object Recognition

https://github.com/JEFfersusu/RT-DSAFDet

Real-Time Dynamic Scale-Aware Fusion Detection Network: Take Road Damage Detection as an example

https://github.com/JEONGHA-SHIN/YOGO_Server_v2

YOLOv3: An Incremental Improvement

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