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/2023-MindSpore-4/Code10/tree/main/ShuffleNet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/2023-MindSpore-4/Code10/tree/main/SwinTransformer

Self-Supervised Learning with Swin Transformers

https://github.com/2023-MindSpore-4/Code10/tree/main/Unet

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/2023-MindSpore-4/Code10/tree/main/YOLOX

YOLOX: Exceeding YOLO Series in 2021

https://github.com/2023-MindSpore-4/Code10/tree/main/YOLOv3

YOLOv3 with Spatial Pyramid Pooling for Object Detection with Unmanned Aerial Vehicles

https://github.com/2023-MindSpore-4/Code10/tree/main/YOLOv4

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/2023-MindSpore-4/Code10/tree/main/YOLOv5

PP-YOLOE: An evolved version of YOLO

https://github.com/2023-MindSpore-4/Code10/tree/main/hardnet

HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS

https://github.com/2023-MindSpore-4/Code11/tree/main/SE-Net

Squeeze-and-Excitation Networks

https://github.com/2023-MindSpore-4/Code11/tree/main/SE_ResNeXt50

Squeeze-and-Excitation Networks

https://github.com/2023-MindSpore-4/Code11/tree/main/Segformer

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/2023-MindSpore-4/Code11/tree/main/SemanticHumanMatting

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/2023-MindSpore-4/Code11/tree/main/resnet50_adv_pruning

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-4/Code11/tree/main/resnetv2_50_frn

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

https://github.com/2023-MindSpore-4/Code11/tree/main/retinanet_resnet101

Focal Loss for Dense Object Detection

https://github.com/2023-MindSpore-4/Code11/tree/main/retinanet_resnet152

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/2023-MindSpore-4/Code11/tree/main/rfcn

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/2023-MindSpore-4/Code11/tree/main/ssc_resnet50

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-4/Code11/tree/main/ssd_resnet_34

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/blip2

BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/clip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/mae

Masked Autoencoders Are Scalable Vision Learners

https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/swin

Swin Transformer V2: Scaling Up Capacity and Resolution

https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_06_convolutional-neural-networks

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_07_convolutional-modern

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/2023-MindSpore-4/Code14/tree/main/ResNeSt50

ResNeSt: Split-Attention Networks

https://github.com/2023-MindSpore-4/Code14/tree/main/SE_ResNeXt50

Squeeze-and-Excitation Networks

https://github.com/2023-MindSpore-4/Code14/tree/main/Segformer

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

https://github.com/2023-MindSpore-4/Code14/tree/main/SpineNet

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/2023-MindSpore-4/Code14/tree/main/res2net

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/2023-MindSpore-4/Code14/tree/main/res2net_deeplabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/2023-MindSpore-4/Code14/tree/main/res2net_faster_rcnn

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

https://github.com/2023-MindSpore-4/Code14/tree/main/res2net_yolov3

YOLOv3: An Incremental Improvement

https://github.com/2023-MindSpore-4/Code14/tree/main/resnetv2_50_frn

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

https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet101

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet152

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

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