Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/YoungXIAO13/PoseContrast

Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild

https://github.com/YourGc/Unet_jinnan2

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/YoushaaMurhij/FasterCNN_Object_Detection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/YudeWang/UNet-Satellite-Image-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/YuefeiZ/PANet

Path Aggregation Network for Instance Segmentation

https://github.com/Yuelong-Yu/ERFNet-Caffe

https://github.com/YuemingJin/TMRNet

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

https://github.com/YufeiHU-fr/obow_ssl

OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning

https://github.com/YugeTen/QMVAE-mmdgm

Multimodal Generative Models for Scalable Weakly-Supervised Learning

https://github.com/YuhuiMa/DFN-tensorflow

Learning a Discriminative Feature Network for Semantic Segmentation

https://github.com/YukangWang/DeepFlux

DeepFlux for Skeletons in the Wild

https://github.com/Yuma82/Paper-Survey

ODAM: Gradient-based instance-specific visual explanations for object detection

https://github.com/YunYang1994/tensorflow-yolov3

YOLOv3: An Incremental Improvement

https://github.com/Yuxiang1995/ICDAR2021_MFD

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Yuzi0123/MindSpore_Yolov5

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/YvanG/VQGAN-CLIP

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ZFTurbo/segmentation_models_3D

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ZH-Lee/Tensorflow-Mobilenet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/ZJCV/KnowledgeReview

Distilling Knowledge via Knowledge Review

https://github.com/ZJCV/ZCls

ResNeSt: Split-Attention Networks

https://github.com/ZJUT-ERCISS/zjut_mindvideo/blob/master/msvideo/models/fairmot.py

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/ZQPei/deep_sort_pytorch

Mask R-CNN

https://github.com/ZTao-z/multiflow-resnet-ssd

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/ZUCCBBQ/yoloV3-

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

https://github.com/ZXdatascience/TextDetectorAndroid

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/ZZR8066/mmdetection-GaofenCompetition

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Zachdr1/CNNs

Deep Residual Learning for Image Recognition

https://github.com/ZackPashkin/YOLOv3-EfficientNet-EffYolo

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/ZackPashkin/text2cartoon-pytorch-CLIP

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/Zahra2351373/Image-Processing-Final-Project

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Zakobian/CT_framework_

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Zanderzt/TensorRT_Mask_RCNN

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

https://github.com/ZeeTsing/Carvana_challenge

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Zehaos/MobileNet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Zeng555/EMV-YOLO

Getting to Know Low-light Images with The Exclusively Dark Dataset

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