Papers with Code 아카이브

2D Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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코드

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

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet_34

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tinydarknet

Mask R-CNN

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/u2net

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

https://github.com/2023-MindSpore-1/ms-code-22

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-1/ms-code-220/tree/main/SRC

SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and Tracking

https://github.com/2023-MindSpore-1/ms-code-220/tree/main/textrcnn

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

https://github.com/2023-MindSpore-1/ms-code-220/tree/main/transformer_xl

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

https://github.com/2023-MindSpore-1/ms-code-222/tree/main/pointRCNN

PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

https://github.com/2023-MindSpore-1/ms-code-223/tree/main/OpenPose

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

https://github.com/2023-MindSpore-1/ms-code-223/tree/main/RetinaNet

Focal Loss for Dense Object Detection

https://github.com/2023-MindSpore-1/ms-code-223/tree/main/ShuffleNet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/2023-MindSpore-1/ms-code-31

YOLOX: Exceeding YOLO Series in 2021

https://github.com/2023-MindSpore-1/ms-code-32

SSD: Single Shot MultiBox Detector

https://github.com/2023-MindSpore-1/ms-code-34

Rethinking Channel Dimensions for Efficient Model Design

https://github.com/2023-MindSpore-1/ms-code-37

Squeeze-and-Excitation Networks

https://github.com/2023-MindSpore-1/ms-code-38

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/2023-MindSpore-1/ms-code-39

Objects as Points

https://github.com/2023-MindSpore-1/ms-code-4/tree/main/PatchCore

Towards Total Recall in Industrial Anomaly Detection

https://github.com/2023-MindSpore-1/ms-code-40

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

https://github.com/2023-MindSpore-1/ms-code-44

Focal Loss for Dense Object Detection

https://github.com/2023-MindSpore-1/ms-code-46

Pyramid Scene Parsing Network

https://github.com/2023-MindSpore-1/ms-code-48

Point Transformer

https://github.com/2023-MindSpore-1/ms-code-5/tree/main/OpenPose

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

https://github.com/2023-MindSpore-1/ms-code-51

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/2023-MindSpore-1/ms-code-59

YOLOv3: An Incremental Improvement

https://github.com/2023-MindSpore-1/ms-code-6/tree/main/CBAM

CBAM: Convolutional Block Attention Module

https://github.com/2023-MindSpore-1/ms-code-6/tree/main/CascadeRCNN

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

https://github.com/2023-MindSpore-1/ms-code-6/tree/main/centernet

CenterNet: Keypoint Triplets for Object Detection

https://github.com/2023-MindSpore-1/ms-code-6/tree/main/centernet_det

CenterNet: Keypoint Triplets for Object Detection

https://github.com/2023-MindSpore-1/ms-code-6/tree/main/cnn_direction_model

Structured Sparse R-CNN for Direct Scene Graph Generation

https://github.com/2023-MindSpore-1/ms-code-60

YOLOv3: An Incremental Improvement

https://github.com/2023-MindSpore-1/ms-code-66

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/2023-MindSpore-1/ms-code-67

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/2023-MindSpore-1/ms-code-69

End-to-End Object Detection with Transformers

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/StyTr-2

StyTr$^2$: Image Style Transfer with Transformers

https://github.com/2023-MindSpore-1/ms-code-7/tree/main/TokenFusion

Multimodal Token Fusion for Vision Transformers

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