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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/MindSpore-paper-code-2/code3/tree/main/wave_mlp

An Image Patch is a Wave: Phase-Aware Vision MLP

https://github.com/MindSpore-paper-code-2/code3/tree/main/yolov3_tiny

YOLOv3: An Incremental Improvement

https://github.com/MindSpore-paper-code-2/code399/tree/main/Transformer

A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation

https://github.com/MindSpore-paper-code-2/code400/tree/main/Efficientnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/MindSpore-paper-code-2/code400/tree/main/MAE

Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training

https://github.com/MindSpore-paper-code-2/code400/tree/main/MobileNet/mobilenetv2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/MindSpore-paper-code-2/code400/tree/main/MobileNet/mobilenetv3

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

https://github.com/MindSpore-paper-code-3/code1/tree/main/Auto-DeepLab

FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search

https://github.com/MindSpore-paper-code-3/code1/tree/main/augvit

How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

https://github.com/MindSpore-paper-code-3/code10/tree/main/FCANet

FcaNet: Frequency Channel Attention Networks

https://github.com/MindSpore-paper-code-3/code10/tree/main/FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/MindSpore-paper-code-3/code10/tree/main/FSAF

Feature Selective Anchor-Free Module for Single-Shot Object Detection

https://github.com/MindSpore-paper-code-3/code10/tree/main/FreeAnchor

FreeAnchor: Learning to Match Anchors for Visual Object Detection

https://github.com/MindSpore-paper-code-3/code10/tree/main/Gold_YOLO

Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism

https://github.com/MindSpore-paper-code-3/code10/tree/main/GridRCNN

An Implementation of Faster RCNN with Study for Region Sampling

https://github.com/MindSpore-paper-code-3/code10/tree/main/fairmot

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/MindSpore-paper-code-3/code10/tree/main/faster_rcnn_dcn

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

https://github.com/MindSpore-paper-code-3/code10/tree/main/foveabox

FoveaBox: Beyond Anchor-based Object Detector

https://github.com/MindSpore-paper-code-3/code10/tree/main/frustum-pointnet

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

https://github.com/MindSpore-paper-code-3/code10/tree/main/hardnet

HarDNet: A Low Memory Traffic Network

https://github.com/MindSpore-paper-code-3/code2/tree/main/Deepsort

StrongSORT: Make DeepSORT Great Again

https://github.com/MindSpore-paper-code-3/code2/tree/main/crnn_seq2seq_ocr

The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS

https://github.com/MindSpore-paper-code-3/code2/tree/main/darknet53

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/MindSpore-paper-code-3/code3/tree/main/FCANet

FcaNet: Frequency Channel Attention Networks

https://github.com/MindSpore-paper-code-3/code3/tree/main/FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceAttribute

Deep Residual Learning for Image Recognition

https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceDetection

Face Detection with End-to-End Integration of a ConvNet and a 3D Model

https://github.com/MindSpore-paper-code-3/code3/tree/main/fairmot

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/MindSpore-paper-code-3/code3/tree/main/faster_rcnn_dcn

Mask R-CNN

https://github.com/MindSpore-paper-code-3/code4/tree/main/HDR-Transformer

A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation

https://github.com/MindSpore-paper-code-3/code4/tree/main/HireMLP

Hire-MLP: Vision MLP via Hierarchical Rearrangement

https://github.com/MindSpore-paper-code-3/code4/tree/main/googlenet

Going Deeper with Convolutions

https://github.com/MindSpore-paper-code-3/code4/tree/main/hardnet

HarDNet: A Low Memory Traffic Network

https://github.com/MindSpore-paper-code-3/code5/tree/main/RefineDet

Development of Fast Refinement Detectors on AI Edge Platforms

https://github.com/MindSpore-paper-code-3/code5/tree/main/ResNeXt

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/MindSpore-paper-code-3/code5/tree/main/SE_ResNeXt50

Squeeze-and-Excitation Networks

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