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/VainF/DeepLabV3Plus-Pytorch

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Vamshi399/CarND-Vehicle-Detection

Deep Residual Learning for Image Recognition

https://github.com/VamsiKrishna1211/gender_age-detection

Deep Residual Learning for Image Recognition

https://github.com/VasuTammisetti/Object-detection-using-YOLO_V3-Darknet

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/VcampSoldiers/Swin-Transformer-Tensorflow

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/VedantDere0104/Pix2Pix_GAN

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Veincore/f-pointnet

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

https://github.com/VeritasXu/Ternary-Federated

Ternary Weight Networks

https://github.com/Veternal1226/YOLO_darknet

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/VhalPurohit/290s

Deep Residual Learning for Image Recognition

https://github.com/ViTAE-Transformer/ViTAE-Transformer

ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias

https://github.com/ViTAE-Transformer/ViTDet

Exploring Plain Vision Transformer Backbones for Object Detection

https://github.com/Video-Streaming-Pipeline/Video-Streaming-Pipeline

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Vignesh-95/cnn-semantic-segmentation-satellite-images

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Vijayabhaskar96/Object-Detection-Algorithms

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

https://github.com/VikParuchuri/surya

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/Vill-Lab/2022-TIFS-CLCXray

https://github.com/Vill-Lab/2022-TIP-HCGA

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/VilledeMontreal/urban-segmentation

Fast-SCNN: Fast Semantic Segmentation Network

https://github.com/VinayBN8997/ResNet-CIFAR10

Deep Residual Learning for Image Recognition

https://github.com/VinayBhupalam/melanoma-detection

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/VinishUchiha/Object-Detection-Using-Yolo4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Violet981/Breast_mass_Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/VisDrone/DroneVehicle

Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning

https://github.com/VishaalMK/VectorDefense

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/Vishal-V/tf-models

Panoptic Feature Pyramid Networks

https://github.com/VisionLearningGroup/DA_Detection

Strong-Weak Distribution Alignment for Adaptive Object Detection

https://github.com/Visual-AI/Mr.DETR

Mr. DETR: Instructive Multi-Route Training for Detection Transformers

https://github.com/Visual-Attention-Network/VAN-Jittor

Visual Attention Network

https://github.com/Visual-Attention-Network/VAN-Segmentation

Visual Attention Network

https://github.com/Visual-Behavior/aloception

End-to-End Object Detection with Transformers

https://github.com/Visual-Behavior/detr-tensorflow

End-to-End Object Detection with Transformers

https://github.com/VisualComputingInstitute/SiamR-CNN

Siam R-CNN: Visual Tracking by Re-Detection

https://github.com/ViswanathaReddyGajjala/EfficientNet-RetinaNet

Deep Residual Learning for Image Recognition

https://github.com/VivekUdupa/CPSC8810-DeepLearning_vkoodli_shonnah

Contextual Action Recognition with R*CNN

https://github.com/Viveksbawa/SARAS-ESAD-Baseline

SSD: Single Shot MultiBox Detector

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