Papers with Code 아카이브

2D Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

27

평가표

0

보존된 평가 기록

0

코드

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

https://github.com/2023-MindSpore-1/ms-code-213/tree/main/faster_rcnn_dcn

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

https://github.com/2023-MindSpore-1/ms-code-213/tree/main/faster_rcnn_ssod

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

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HMR

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HRNetW48_cls

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HRNetW48_seg

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HireMLP

Hire-MLP: Vision MLP via Hierarchical Rearrangement

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/frustum-pointnet

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

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/googlenet

Going Deeper with Convolutions

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/hardnet

HarDNet: A Low Memory Traffic Network

https://github.com/2023-MindSpore-1/ms-code-214/tree/main/ibnnet

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PSPNet

Pyramid Scene Parsing Network

https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pointpillars

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pointtransformer

Point Transformer

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/PVAnet

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

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RDN

Residual Dense Network for Image Restoration

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/RefineDet

RefineDetLite: A Lightweight One-stage Object Detection Framework for CPU-only Devices

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/ResNeSt50

ResNeSt: Split-Attention Networks

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/SE-Net

Squeeze-and-Excitation Networks

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/SE_ResNeXt50

Squeeze-and-Excitation Networks

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/res2net

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/res2net_deeplabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/res2net_faster_rcnn

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

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/res2net_yolov3

YOLOX: Exceeding YOLO Series in 2021

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnet50_adv_pruning

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnetv2_50_frn

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

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet101

Focal Loss for Dense Object Detection

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet152

Focal Loss for Dense Object Detection

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/rfcn

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

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/StyTr-2

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/TokenFusion

Multimodal Token Fusion for Vision Transformers

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/UNet3%2B

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ViG

Vision GNN: An Image is Worth Graph of Nodes

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_mobilenetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_mobilenetV2_FPNlite

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet34

Deep Residual Learning for Image Recognition

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