Papers with Code 아카이브

Object Detection

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

39

평가표

124

보존된 평가 기록

977

코드

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

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/conv/DepthwiseSeparableConvolution.py

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/conv/HorNet.py

HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/conv/MBConv.py

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/mindspore-courses/MindSpore-classification

Deep Residual Learning for Image Recognition

https://github.com/mindspore-courses/heads-on-mindspore/tree/main/1-best-practice/models

Deep Residual Learning for Image Recognition

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/convnext.py

A ConvNet for the 2020s

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/efficientnet.py

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/googlenet.py

Going Deeper with Convolutions

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/mnasnet.py

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/mobilenet_v1.py

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/mobilenet_v2.py

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/mobilenet_v3.py

Searching for MobileNetV3

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/res2net.py

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/resnet.py

Deep Residual Learning for Image Recognition

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/rexnet.py

Rethinking Channel Dimensions for Efficient Model Design

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/shufflenetv1.py

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/shufflenetv2.py

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/swin_transformer.py

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/mindspore-lab/mindone

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/minerva-ml/open-solution-mapping-challenge

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ming71/UCAS-AOD-benchmark

DOTA: A Large-scale Dataset for Object Detection in Aerial Images

https://github.com/mingxingtan/efficientnet

Deep Residual Learning for Image Recognition

https://github.com/mingxingtan/mnasnet

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/minhanh151/pre

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/minhanh151/respro

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/minhcong0891/wheat-detection

YOLOv3: An Incremental Improvement

https://github.com/minhdua/PHONES

YOLO9000: Better, Faster, Stronger

https://github.com/minhhuu291/Logo-Recognition

SSD: Single Shot MultiBox Detector

https://github.com/minhto2802/keras-shufflenet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/minoring/mobilenet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/minus31/BlazeFace

Pooling Pyramid Network for Object Detection

https://github.com/miquel-espinosa/samantics

No time to train! Training-Free Reference-Based Instance Segmentation

https://github.com/miramind/efficientnets_pytorch

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/mirnadaye/Brain_Tumor_Project

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/mirthai/csa-net

Non-local Neural Networks

https://github.com/mirzaevinom/data_science_bowl_2018

Mask R-CNN

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