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/KarimaCha/CNN_Trust_Prediction

Deep Residual Learning for Image Recognition

https://github.com/KarinaKorsgaard/Bartender-AI-OpenPose-Osc-Sender

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

https://github.com/KarthikDulam/U-NetCNN

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Kartik-Aggarwal/Real-Time-Traffic-Sign-Detection

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

https://github.com/KatherineBaq/NMA-CN2021-fMRI_vs_DNNs

Deep Residual Learning for Image Recognition

https://github.com/Kaushal28/Bengali-AI

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

https://github.com/Kaushalya/medclip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/Kazuhito00/sahi-yolox-onnx-sample

Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection

https://github.com/Keep-Passion/SESF-Fuse

End-to-End Learning for Simultaneously Generating Decision Map and Multi-Focus Image Fusion Result

https://github.com/KellerJordan/ResNet-PyTorch-CIFAR10

Deep Residual Learning for Image Recognition

https://github.com/KellyHwong/rethinking_generalization

Deep Residual Learning for Image Recognition

https://github.com/Kenneth-Wong/tf-faster-rcnn

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

https://github.com/KennithLi/Awesome-Zero-Shot-Object-Detection

Zero-Shot Object Detection by Hybrid Region Embedding

https://github.com/KentaItakura/CNN-classification-using-random-erasing-and-cut-out

Random Erasing Data Augmentation

https://github.com/KernelA/pytorch-point-transformer

Point Transformer

https://github.com/Keshavkant/YOLO_Toll

YOLOv3: An Incremental Improvement

https://github.com/Kev1nZheng/yolov_mask

YOLOv3: An Incremental Improvement

https://github.com/KevinKecc/MAL

Multiple Anchor Learning for Visual Object Detection

https://github.com/KeyG518/Unet_Cell_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Khalifa-2020/centernet1

Objects as Points

https://github.com/Khalifa-2020/centernet2

Objects as Points

https://github.com/KhusDM/PointNetTree

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/KieDani/SegformerPlusPlus

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/Kin-Zhang/mmfn

Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

https://github.com/KingBoyBIT/yolov3test

YOLO9000: Better, Faster, Stronger

https://github.com/KingGugu/YOLOv4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Kinpzz/RCRNet-Pytorch

Semi-Supervised Video Salient Object Detection Using Pseudo-Labels

https://github.com/KiranAkadas/My_Pointnet_v2

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

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

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

https://github.com/Kissonis/Quantum_test

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Knight825/models-pytorch

Squeeze-and-Excitation Networks

https://github.com/KnowledgeDiscovery/FaceSec

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/KookHoiKim/GroupFree3dBaseline

Group-Free 3D Object Detection via Transformers

https://github.com/KostadinovShalon/oln-mask

Learning Open-World Object Proposals without Learning to Classify

https://github.com/Krupal09/MaskRCNN-Demo

Mask R-CNN

https://github.com/Kungbohan/EECSMed

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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