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-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/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

https://github.com/Mind23-2/MindCode-101/tree/main/m2det

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

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

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/Mind23-2/MindCode-101/tree/main/mobilenetV3_small_x1_0

Searching for MobileNetV3

https://github.com/Mind23-2/MindCode-101/tree/main/mobilenetv3_large

Searching for MobileNetV3

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

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

https://github.com/Mind23-2/MindCode-102

Searching for MobileNetV3

https://github.com/Mind23-2/MindCode-103

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

https://github.com/Mind23-2/MindCode-105

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/Mind23-2/MindCode-108

MetaFormer Is Actually What You Need for Vision

https://github.com/Mind23-2/MindCode-109

PVT: Point-Voxel Transformer for Point Cloud Learning

https://github.com/Mind23-2/MindCode-110

PVT v2: Improved Baselines with Pyramid Vision Transformer

https://github.com/Mind23-2/MindCode-114

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/Mind23-2/MindCode-115

ResNeSt: Split-Attention Networks

https://github.com/Mind23-2/MindCode-117

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/Mind23-2/MindCode-123

XCiT: Cross-Covariance Image Transformers

https://github.com/Mind23-2/MindCode-127

YOLOv3: An Incremental Improvement

https://github.com/Mind23-2/MindCode-128

Efficient Teacher: Semi-Supervised Object Detection for YOLOv5

https://github.com/Mind23-2/MindCode-129

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

https://github.com/Mind23-2/MindCode-130

PP-YOLOE: An evolved version of YOLO

https://github.com/Mind23-2/MindCode-15

Objects as Points

https://github.com/Mind23-2/MindCode-155

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/Mind23-2/MindCode-165

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/Mind23-2/MindCode-168

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/Mind23-2/MindCode-170

Co-Scale Conv-Attentional Image Transformers

https://github.com/Mind23-2/MindCode-176

Focal Loss for Dense Object Detection

https://github.com/Mind23-2/MindCode-177

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

https://github.com/Mind23-2/MindCode-180

Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving

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

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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

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

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