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2D Classification

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

https://github.com/Vaibhavck/Style-Transfer-VGG19

A Neural Algorithm of Artistic Style

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/Varunittigi/Neural-Style-transfer

A Neural Algorithm of Artistic Style

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/VectorInstitute/VLDBench

VLDBench: Vision Language Models Disinformation Detection Benchmark

https://github.com/VedantDere0104/Cycle_GAN

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/VedantDere0104/Pix2Pix_GAN

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/VegB/VLN-Transformer

Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation

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/Vernacular-ai/Multimodal-Slu

Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning

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/VietHoang1512/khmer-nltk

Multilingual Open Text Release 1: Public Domain News in 44 Languages

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/Vill-Lab/2022-TIP-HCGA

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/Vill-Lab/forkOfStyleShot

StyleShot: A Snapshot on Any Style

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/VinceMarron/style_transfer

A Neural Algorithm of Artistic Style

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

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Vinr1ch/cs390-lab2

A Neural Algorithm of Artistic Style

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

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

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

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