Papers with Code 아카이브

2D Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

15

평가표

20

보존된 평가 기록

57

코드

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

https://github.com/Mind23-2/MindCode-22

ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

https://github.com/Mind23-2/MindCode-28

Objects as Points

https://github.com/Mind23-2/MindCode-29

Objects as Points

https://github.com/Mind23-2/MindCode-3

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/Mind23-2/MindCode-3/tree/main/HRNetW48_cls

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/Mind23-2/MindCode-3/tree/main/HRNetW48_seg

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/Mind23-2/MindCode-3/tree/main/HireMLP

Hire-MLP: Vision MLP via Hierarchical Rearrangement

https://github.com/Mind23-2/MindCode-3/tree/main/I3D

Non-local Neural Networks

https://github.com/Mind23-2/MindCode-3/tree/main/ICNet

ICNet for Real-Time Semantic Segmentation on High-Resolution Images

https://github.com/Mind23-2/MindCode-3/tree/main/ISyNet

ISyNet: Convolutional Neural Networks design for AI accelerator

https://github.com/Mind23-2/MindCode-3/tree/main/NFNet

MaxViT: Multi-Axis Vision Transformer

https://github.com/Mind23-2/MindCode-3/tree/main/hed

Holistically-Nested Edge Detection

https://github.com/Mind23-2/MindCode-3/tree/main/hlg

Vision Transformers: From Semantic Segmentation to Dense Prediction

https://github.com/Mind23-2/MindCode-3/tree/main/ibnnet

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

https://github.com/Mind23-2/MindCode-3/tree/main/lresnet100e_ir

Deep Residual Learning for Image Recognition

https://github.com/Mind23-2/MindCode-3/tree/main/m2det

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

https://github.com/Mind23-2/MindCode-3/tree/main/mnasnet

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/Mind23-2/MindCode-3/tree/main/mobilenetV3_small_x1_0

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Mind23-2/MindCode-3/tree/main/mobilenetv3_large

Searching for MobileNetV3

https://github.com/Mind23-2/MindCode-3/tree/main/nas-fpn

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

https://github.com/Mind23-2/MindCode-3/tree/main/nasnet

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/Mind23-2/MindCode-33

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Mind23-2/MindCode-36

EfficientDet: Scalable and Efficient Object Detection

https://github.com/Mind23-2/MindCode-37

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/Mind23-2/MindCode-38

https://github.com/Mind23-2/MindCode-39

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Mind23-2/MindCode-4/tree/main/hrnet

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/Mind23-2/MindCode-40

https://github.com/Mind23-2/MindCode-42

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

https://github.com/Mind23-2/MindCode-43

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

https://github.com/Mind23-2/MindCode-44

Fast-SCNN: Fast Semantic Segmentation Network

https://github.com/Mind23-2/MindCode-46

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Mind23-2/MindCode-47

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Mind23-2/MindCode-5/tree/main/PSPNet

Pyramid Scene Parsing Network

https://github.com/Mind23-2/MindCode-5/tree/main/PVAnet

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

https://github.com/Mind23-2/MindCode-5/tree/main/pointpillars

Sparse PointPillars: Maintaining and Exploiting Input Sparsity to Improve Runtime on Embedded Systems

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