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2D Classification

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코드

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

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/blip

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

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/blip_2

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/clip

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/convnext

A ConvNet for the 2020s

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/convnextv2

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

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/detr

End-to-End Object Detection with Transformers

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/efficientnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/fnet

Focal Modulation Networks

https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/groupvit

GroupViT: Semantic Segmentation Emerges from Text Supervision

https://github.com/2024-MindSpore-1/Code2/tree/main/wangyikai/EIP-mindspore

Multimodal Token Fusion for Vision Transformers

https://github.com/2024-MindSpore-1/Code3/tree/main/MaxViT

MaxViT: Multi-Axis Vision Transformer

https://github.com/2024-MindSpore-1/Code3/tree/main/ResNet18

Augmentation for small object detection

https://github.com/2024-MindSpore-1/Code4/tree/main/CycleGAN

CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion

https://github.com/2024-MindSpore-1/Code4/tree/main/DeepLabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/2024-MindSpore-1/Code4/tree/main/Efficientnet

EfficientDet: Scalable and Efficient Object Detection

https://github.com/2024-MindSpore-1/Code4/tree/main/FasterRCNN

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

https://github.com/2024-MindSpore-1/Code4/tree/main/MobileNet/mobilenetv1

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/2024-MindSpore-1/Code4/tree/main/MobileNet/mobilenetv2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/2024-MindSpore-1/Code4/tree/main/MobileNet/mobilenetv3

Searching for MobileNetV3

https://github.com/2024-MindSpore-1/Code4/tree/main/OpenPose

OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/2024-MindSpore-1/Code4/tree/main/RetinaFace_ResNet50

Focal Loss for Dense Object Detection

https://github.com/2024-MindSpore-1/Code4/tree/main/RetinaNet

Focal Loss for Dense Object Detection

https://github.com/2024-MindSpore-1/Code4/tree/main/ShuffleNet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/2024-MindSpore-1/Code4/tree/main/SwinTransformer

Self-Supervised Learning with Swin Transformers

https://github.com/2024-MindSpore-1/Code5/tree/main/adelaide_ea

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/2024-MindSpore-1/Code6/tree/main/augvit

How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

https://github.com/2024-MindSpore-1/Code7/tree/main/CBAM

CBAM: Convolutional Block Attention Module

https://github.com/2024-MindSpore-1/Code7/tree/main/centernet

CenterNet: Keypoint Triplets for Object Detection

https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_det

CenterNet: Keypoint Triplets for Object Detection

https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet101

Deep Residual Learning for Image Recognition

https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet50_v1

Deep Residual Learning for Image Recognition

https://github.com/2024-MindSpore-1/Code7/tree/main/cnn_direction_model

Structured Sparse R-CNN for Direct Scene Graph Generation

https://github.com/2anchao/VovJpu

An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection

https://github.com/2gunsu/monocon-pytorch

Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection

https://github.com/2manslkh/korean-food-classification-flask

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/329tyson/fai-ssl-challenge

Scaling and Benchmarking Self-Supervised Visual Representation Learning

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