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2D Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

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

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/SinGAN

SinGAN-Seg: Synthetic training data generation for medical image segmentation

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

StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion

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/Code12/tree/main/ganleilei/CleanLabelBackdoorAttackMindspore-master

Triggerless Backdoor Attack for NLP Tasks with Clean Labels

https://github.com/2023-MindSpore-4/Code12/tree/main/liliang/ssa

Unsupervised Coherent Video Cartoonization with Perceptual Motion Consistency

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/SinGAN

SinGAN-Seg: Synthetic training data generation for medical image segmentation

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

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

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

https://github.com/2023-MindSpore-4/Code15/tree/main/wangyikai/TokenFusion

Multimodal Token Fusion for Vision Transformers

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