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/kochlisGit/Custom-Small-Logo-Detection

EfficientDet: Scalable and Efficient Object Detection

https://github.com/kochlisGit/LogoLens

EfficientDet: Scalable and Efficient Object Detection

https://github.com/koechslin/swin-transformer-semantic-segmentation

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/kohillyang/mx-detection

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/kondukberna/Car_Detection

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/kongzhiyou/darknet-master

YOLO9000: Better, Faster, Stronger

https://github.com/konny0311/ssd_trial

SSD: Single Shot MultiBox Detector

https://github.com/konny0311/unet_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/konstantinos-p/image_classification_SOTA

Deep Residual Learning for Image Recognition

https://github.com/konyul/mmdet3d

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/koriavinash1/Fetal-Brain-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/kornia/kornia

Focal Loss for Dense Object Detection

https://github.com/koshian2/OctConv-TFKeras

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/koshian2/PeleeNet-Keras

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

https://github.com/koshian2/ResNet-MultipleFramework

Deep Residual Learning for Image Recognition

https://github.com/kpullak/DefectSegmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/krantirk/py-faster-rcnn

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

https://github.com/krishnac7/OpenPose

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

https://github.com/krishnakarthi/COVID-19_Prediction

Deep Residual Learning for Image Recognition

https://github.com/kritanu82/healthif.ai

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/kritiksoman/GIMP-ML

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

https://github.com/krstevskipetar/SSDLite320-MobileNetV3-object-detection

SSD: Single Shot MultiBox Detector

https://github.com/krullgit/realsense_3d_detection

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/krumo/Detectron-DA-Faster-RCNN

Domain Adaptive Faster R-CNN for Object Detection in the Wild

https://github.com/krumo/Domain-Adaptive-Faster-RCNN-PyTorch

Domain Adaptive Faster R-CNN for Object Detection in the Wild

https://github.com/krutikabapat/Deep-Learning-based-OpenPose-Estimation

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

https://github.com/ksheeraj/CS256-AI-ObjectDetection

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

https://github.com/kshitij1489/object-tracking

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/kshmelkov/incremental_detectors

Incremental Learning of Object Detectors without Catastrophic Forgetting

https://github.com/ksm26/spatiotemporal-predictions

nuScenes: A multimodal dataset for autonomous driving

https://github.com/ktlhtn/AutoSTAnnot

Object Detection in Equirectangular Panorama

https://github.com/ktw361/mmdetection_impl

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/kuan-wang/pytorch-mobilenet-v3

Searching for MobileNetV3

https://github.com/kuangliu/pytorch-fpn

Feature Pyramid Networks for Object Detection

https://github.com/kuangzijian/Flow-Based-Video-Matting

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/kujason/ip_basic

Joint 3D Proposal Generation and Object Detection from View Aggregation

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