Papers with Code 아카이브

16k

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

6

평가표

1

보존된 평가 기록

1

코드

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

https://github.com/2023-MindSpore-4/Code14/tree/main/SE_ResNeXt50

Squeeze-and-Excitation Networks

https://github.com/2023-MindSpore-4/Code14/tree/main/SRGAN

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

https://github.com/2023-MindSpore-4/Code14/tree/main/SpineNet

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/2023-MindSpore-4/Code14/tree/main/res2net

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/2023-MindSpore-4/Code14/tree/main/res2net_deeplabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/2023-MindSpore-4/Code14/tree/main/res2net_faster_rcnn

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

https://github.com/2023-MindSpore-4/Code14/tree/main/res2net_yolov3

YOLOv3: An Incremental Improvement

https://github.com/2023-MindSpore-4/Code14/tree/main/resnetv2_50_frn

Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks

https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet101

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet152

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/2023-MindSpore-4/Code14/tree/main/rfcn

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/2023-MindSpore-4/Code15/tree/main/wangyikai/TokenFusion

Multimodal Token Fusion for Vision Transformers

https://github.com/2023-MindSpore-4/Code2/tree/main/DBPN

Deep Back-Projection Networks for Single Image Super-resolution

https://github.com/2023-MindSpore-4/Code2/tree/main/darknet53

RealNet: Combining Optimized Object Detection with Information Fusion Depth Estimation Co-Design Method on IoT

https://github.com/2023-MindSpore-4/Code3/tree/main/Deepsort

StrongSORT: Make DeepSORT Great Again

https://github.com/2023-MindSpore-4/Code3/tree/main/E-NET

https://github.com/2023-MindSpore-4/Code3/tree/main/EfficientDet_d0

EfficientDet: Scalable and Efficient Object Detection

https://github.com/2023-MindSpore-4/Code3/tree/main/detr

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

https://github.com/2023-MindSpore-4/Code3/tree/main/efficientnet-b0

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/2023-MindSpore-4/Code4/tree/main/GLNet-TCYB2022-MindSpore

Global-and-Local Collaborative Learning for Co-Salient Object Detection

https://github.com/2023-MindSpore-4/Code4/tree/main/tnet_mindspore

Does Thermal Really Always Matter for RGB-T Salient Object Detection?

https://github.com/2023-MindSpore-4/Code4/tree/main/wslnet

A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels

https://github.com/2023-MindSpore-4/Code6/tree/main/PVAnet

PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

https://github.com/2023-MindSpore-4/Code6/tree/main/RCAN

Revisiting RCAN: Improved Training for Image Super-Resolution

https://github.com/2023-MindSpore-4/Code6/tree/main/RDN

Residual Dense Network for Image Super-Resolution

https://github.com/2023-MindSpore-4/Code6/tree/main/RefineDet

FSSD: Feature Fusion Single Shot Multibox Detector

https://github.com/2023-MindSpore-4/Code6/tree/main/ResNeSt50

ResNeSt: Split-Attention Networks

https://github.com/2023-MindSpore-4/Code7/tree/main/SRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/2023-MindSpore-4/Code7/tree/main/srcnn

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_mobilenetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_mobilenetV2_FPNlite

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet34

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet_34

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-4/Code8/tree/main/3D_DenseNet

DenseNet: Implementing Efficient ConvNet Descriptor Pyramids

https://github.com/2023-MindSpore-4/Code8/tree/main/CBAM

CBAM: Convolutional Block Attention Module

https://github.com/2023-MindSpore-4/Code8/tree/main/augvit

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

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