Papers with Code 아카이브

2D Classification

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코드

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

https://github.com/MeAmarP/Fruit-Classification

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Media-Smart/SKU110K-DenseDet

Precise Detection in Densely Packed Scenes

https://github.com/Media-Smart/vedaseg

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/MediaTek-NeuroPilot/mai21-learned-smartphone-isp

Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency

https://github.com/MegEngine/Models/tree/master/official/vision/classification

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

https://github.com/MegEngine/Models/tree/master/official/vision/classification/resnet

Deep Residual Learning for Image Recognition

https://github.com/MegEngine/Models/tree/master/official/vision/detection

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

https://github.com/MegEngine/YOLOX

YOLOX: Exceeding YOLO Series in 2021

https://github.com/Megatron8010/science_concierge

Science Concierge: A fast content-based recommendation system for scientific publications

https://github.com/Meghanshu-Bhatt/Neural-artistic-style-transfer

A Neural Algorithm of Artistic Style

https://github.com/MehmetBarutcu/PHM-Paper-Implementation

Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters

https://github.com/MeiguangJin/Learning-to-Extract-a-Video-Sequence-from-a-Single-Motion-Blurred-Image

Learning to Extract a Video Sequence from a Single Motion-Blurred Image

https://github.com/MenGuangwen-CN-0411/Simple-Use-of-SSD

SSD: Single Shot MultiBox Detector

https://github.com/MenghaoGuo/EANet

Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks

https://github.com/Meowuu7/Point-Transformer

Point Transformer

https://github.com/Mephisto405/Learning-Loss-for-Active-Learning

Learning Loss for Active Learning

https://github.com/Merenguelkl/Reverse_Disstilation

Anomaly Detection via Reverse Distillation from One-Class Embedding

https://github.com/Mersive-Technologies/yolov3

YOLOv3: An Incremental Improvement

https://github.com/MiX-S/resnet_cifar10

Deep Residual Learning for Image Recognition

https://github.com/MichaelWangfc/distributed-tensorflow-resnet

Deep Residual Learning for Image Recognition

https://github.com/Michedev/Prototypical-Networks

Prototypical Networks for Few-shot Learning

https://github.com/Microsoft/singleshotpose

BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth

https://github.com/MikelBros/Yolov3_DeepSort_Pytorch

YOLOv3: An Incremental Improvement

https://github.com/Miltos-90/UNet_Biomedical_Image_Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Min-Jaewon/PSLD_PAG

Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance

https://github.com/Mind23-2/MindCode-1

Side-Aware Boundary Localization for More Precise Object Detection

https://github.com/Mind23-2/MindCode-101

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/Mind23-2/MindCode-101/tree/main/HRNetW48_cls

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/Mind23-2/MindCode-101/tree/main/IPT

Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy

https://github.com/Mind23-2/MindCode-101/tree/main/ISyNet

ISyNet: Convolutional Neural Networks design for AI accelerator

https://github.com/Mind23-2/MindCode-101/tree/main/LearningToSeeInTheDark

Learning to See in the Dark

https://github.com/Mind23-2/MindCode-101/tree/main/MAML

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/Mind23-2/MindCode-101/tree/main/MIMO-UNet

Rethinking Coarse-to-Fine Approach in Single Image Deblurring

https://github.com/Mind23-2/MindCode-101/tree/main/NFNet

MaxViT: Multi-Axis Vision Transformer

https://github.com/Mind23-2/MindCode-101/tree/main/Non_local

Non-local Neural Networks

https://github.com/Mind23-2/MindCode-101/tree/main/lresnet100e_ir

Deep Residual Learning for Image Recognition

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