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/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-118

Utilizing Self-supervised Representations for MOS Prediction

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-138

Language Models are Few-Shot Learners

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-166

Utilizing Self-supervised Representations for MOS Prediction

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-175

Utilizing Self-supervised Representations for MOS Prediction

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

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

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