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/ForrestPi/GHM_Loss

Gradient Harmonized Single-stage Detector

https://github.com/FraCorti/Deep_Subnetworks_for_Dynamic_Resource_Constraints

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/FrancescoSaverioZuppichini/ResNet

Deep Residual Learning for Image Recognition

https://github.com/FrancisCrickInstitute/Etch-a-Cell-Nuclear-Envelope

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/FreddeFrallan/Multilingual-CLIP

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/Fw9wef/DetectorGAN

Generative Modeling for Small-Data Object Detection

https://github.com/G0rav/Automatic_Background_Removal

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/GAIA-vision/GAIA-det

GAIA: A Transfer Learning System of Object Detection that Fits Your Needs

https://github.com/GAOwy123/py-faster-rcnn

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

https://github.com/GH3927/Mask-RCNN-applied-to-cranes

Mask R-CNN

https://github.com/GKalliatakis/Keras-VGG16-places365

Deep Residual Learning for Image Recognition

https://github.com/GT-RIPL/L2C

Learning to Cluster for Proposal-Free Instance Segmentation

https://github.com/GXYM/Focal-loss

Focal Loss for Dense Object Detection

https://github.com/GXYM/GHM_Loss

Gradient Harmonized Single-stage Detector

https://github.com/GZQ0723/YoloV4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Gabriel-Macias/robust_frcnn

A Robust Learning Approach to Domain Adaptive Object Detection

https://github.com/Gahyeonkim09/AAPL

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/Gaurav14cs17/YOLOE

Learning Open-World Object Proposals without Learning to Classify

https://github.com/Gavino7/YOLOv3set

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Gedeon-m-gedus/compressed_object_detection

Compressed Object Detection

https://github.com/GeeGeeWP/SCML

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/GeekAlexis/Guardian

Efficient ConvNet-based Object Detection for Unmanned Aerial Vehicles by Selective Tile Processing

https://github.com/GenesisDCarmen/C_Reconocimiento_Facial

Scalable Object Detection using Deep Neural Networks

https://github.com/GeoDQ/Seismic-image-segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/George-Holbrow-Wilshaw/Human-Protein-Atlas-Kaggle

PCL: Proposal Cluster Learning for Weakly Supervised Object Detection

https://github.com/George-Ogden/Mask-RCNN

Mask R-CNN

https://github.com/GewelsJI/PNS-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Ggs1mida/Awesome-DataFusion

nuScenes: A multimodal dataset for autonomous driving

https://github.com/Ggs1mida/Awesome-PointCloudRegistration

nuScenes: A multimodal dataset for autonomous driving

https://github.com/GiaKhangLuu/YOLOv1_from_scratch

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

https://github.com/GianRomani/YOLOv3-Neural-Networks-project-

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

https://github.com/GiantPandaCV/yolov3-point

YOLOv3: An Incremental Improvement

https://github.com/GibranBenitez/IPN-hand

HarDNet: A Low Memory Traffic Network

https://github.com/Gideon0805/Tensorflow1.15-Model-Pruning

Squeeze-and-Excitation Networks

https://github.com/Gideon0805/Tensorflow_Model_Pruning

Squeeze-and-Excitation Networks

https://github.com/GiovanniPasq/DA-Faster-RCNN

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

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