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-ai/models/tree/master/research/cv/nas-fpn

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

https://github.com/mindspore-ai/models/tree/master/research/cv/rcnn

Rich feature hierarchies for accurate object detection and semantic segmentation

https://github.com/mindspore-ai/models/tree/master/research/cv/res2net

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/mindspore-ai/models/tree/master/research/cv/resnetv2_50_frn

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

https://github.com/mindspore-ai/models/tree/master/research/cv/ssd_resnet50

SSD: Single Shot MultiBox Detector

https://github.com/mindspore-ai/models/tree/master/research/cv/tsm

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/mindspore-ai/models/tree/master/research/cv/u2net

U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection

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

CBAM: Convolutional Block Attention Module

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

Contextual Transformer Networks for Visual Recognition

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

Coordinate Attention for Efficient Mobile Network Design

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

CCNet: Criss-Cross Attention for Semantic Segmentation

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

CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention

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

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

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

EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network

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

Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks

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

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

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/MobileViTv2Attention.py

Separable Self-attention for Mobile Vision Transformers

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

Non-deep Networks

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

Squeeze-and-Excitation Networks

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

Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks

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

SA-Net: Shuffle Attention for Deep Convolutional Neural Networks

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

Rotate to Attend: Convolutional Triplet Attention Module

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

Co-Scale Conv-Attentional Image Transformers

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

ConTNet: Why not use convolution and transformer at the same time?

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

Vision Transformers with Hierarchical Attention

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/MobileNetV2.py

MobileNetV2: Inverted Residuals and Linear Bottlenecks

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

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

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

Rethinking Spatial Dimensions of Vision Transformers

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

PVT v2: Improved Baselines with Pyramid Vision Transformer

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

Augmenting Convolutional networks with attention-based aggregation

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

Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/convnextv2.py

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

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

Deep Residual Learning for Image Recognition

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

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/swin_transformer_v2.py

Swin Transformer V2: Scaling Up Capacity and Resolution

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

CondConv: Conditionally Parameterized Convolutions for Efficient Inference

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