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/2023-MindSpore-1/ms-code-216/tree/main/res2net_deeplabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/res2net_faster_rcnn

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

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/res2net_yolov3

YOLOX: Exceeding YOLO Series in 2021

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnet50_adv_pruning

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnetv2_50_frn

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

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet101

Focal Loss for Dense Object Detection

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/retinanet_resnet152

Focal Loss for Dense Object Detection

https://github.com/2023-MindSpore-1/ms-code-216/tree/main/rfcn

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

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

Multimodal Token Fusion for Vision Transformers

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/UNet3%2B

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

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

Vision GNN: An Image is Worth Graph of Nodes

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_mobilenetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_mobilenetV2_FPNlite

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet34

Deep Residual Learning for Image Recognition

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-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/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-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-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

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