Papers with Code 아카이브

2D Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

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

https://github.com/LilyHu/image_segmentation_chromosomes

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/LinHuiTeng/TrashClassifier

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/LinRiver/YOLOv3-on-LISA-Traffic-Sign-Detection-with-darknet

Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

https://github.com/LinZhuoChen/pointnet2_multi_gpu

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

https://github.com/Linby1103/Darknet-Python-Interface

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Line290/EDD-third-party

Image-based table recognition: data, model, and evaluation

https://github.com/Linxuxin/mmdetection-mine

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Linyou/taichi-ngp-renderer

Instant Neural Graphics Primitives with a Multiresolution Hash Encoding

https://github.com/Little-BigMan/YOLOv4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/LiuShenLan/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Liuyubao/transfer-learning

Going Deeper with Convolutions

https://github.com/Lkruitwagen/remote-sensing-solar-pv

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Lmath11/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/Lmonster/darknet

YOLOv3: An Incremental Improvement

https://github.com/LokeZhou/mydetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/Lopezurrutia/DSB_2018

Mask R-CNN

https://github.com/LorisNanni/Improving-existing-segmentators-performance-with-zero-shot-segmentators

https://github.com/Lornatang/PyTorch-CycleGAN

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

https://github.com/Lornatang/PyTorch-yolov3

YOLOv3: An Incremental Improvement

https://github.com/Lornatang/RFB_ESRGAN-PyTorch

Perceptual Extreme Super Resolution Network with Receptive Field Block

https://github.com/Lornatang/ResNet-PyTorch

Deep Residual Learning for Image Recognition

https://github.com/Lornatang/YOLOv3-PyTorch

YOLOv3: An Incremental Improvement

https://github.com/Lornatang/YOLOv4-PyTorch

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/LouieYang/deep-photo-styletransfer-tf

Deep Photo Style Transfer

https://github.com/LouieYang/stroke-controllable-fast-style-transfer

Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields

https://github.com/Louis24/Segmentation

MegDet: A Large Mini-Batch Object Detector

https://github.com/Lu-Hsuan/A-Neural-Algorithm-of-Artistic-Style

A Neural Algorithm of Artistic Style

https://github.com/LuanAdemi/VisualGo

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/LubdaMax/Data-Science-1

WikiHow: A Large Scale Text Summarization Dataset

https://github.com/LucaHermes/graph-unet-traffic-prediction

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/LucasSheng/avatar-net

Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration

https://github.com/Luckygyana/Invo-AI

Weighted boxes fusion: Ensembling boxes from different object detection models

https://github.com/LuigiRussoDev/Covid19Detection

Deep Residual Learning for Image Recognition

https://github.com/LuigiRussoDev/ResNets

Deep Residual Learning for Image Recognition

https://github.com/LukeBolly/tf-tspn

Conditional Set Generation with Transformers

https://github.com/Lw510107/PointNet

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

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