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/MindSpore-paper-code-3/code8/tree/main/Deepsort

StrongSORT: Make DeepSORT Great Again

https://github.com/MindSpore-paper-code-3/code8/tree/main/EGnet

EGNet:Edge Guidance Network for Salient Object Detection

https://github.com/MindSpore-paper-code-3/code8/tree/main/EfficientDet_d0

EfficientDet: Scalable and Efficient Object Detection

https://github.com/MindSpore-paper-code-3/code8/tree/main/FCANet

FcaNet: Frequency Channel Attention Networks

https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceAttribute

Deep Residual Learning for Image Recognition

https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceDetection

Face Detection with End-to-End Integration of a ConvNet and a 3D Model

https://github.com/MindSpore-paper-code-3/code8/tree/main/cspdarknet53

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/MindSpore-paper-code-3/code8/tree/main/darknet53

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/MindSpore-paper-code-3/code8/tree/main/detr

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

https://github.com/MindSpore-paper-code-3/code8/tree/main/efficientnet-b0

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/MindSpore-paper-code-3/code8/tree/main/erfnet

https://github.com/MindSpore-paper-code-3/code8/tree/main/fairmot

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/MindSpore-paper-code-3/code8/tree/main/faster_rcnn_dcn

Deformable ConvNets v2: More Deformable, Better Results

https://github.com/MindSpore-paper-code-3/code8/tree/main/fastscnn

Fast-SCNN: Fast Semantic Segmentation Network

https://github.com/MindSpore-paper-code-3/code9/tree/main/DYR

DyRA: Portable Dynamic Resolution Adjustment Network for Existing Detectors

https://github.com/MindSpore-paper-code-3/code9/tree/main/DeepLabv3

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/MindSpore-paper-code-3/code9/tree/main/Efficientnet

EfficientDet: Scalable and Efficient Object Detection

https://github.com/MindSpore-paper-code-3/code9/tree/main/FasterRCNN

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

https://github.com/MindSpore-paper-code-3/code9/tree/main/MaskRCNN

Mask R-CNN

https://github.com/MindSpore-paper-code-3/code9/tree/main/ResNet

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/MindSpore-paper-code-3/code9/tree/main/SSD

SSD: Single Shot MultiBox Detector

https://github.com/MindSpore-paper-code-3/code9/tree/main/ShuffleNet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/MindSpore-scientific-2/code-10/tree/main/STCrowd

STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes

https://github.com/MindSpore-scientific-2/code-10/tree/main/SV-X-Softmax

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/MindSpore-scientific-2/code-11/tree/main/STCrowd

STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes

https://github.com/MindSpore-scientific-2/code-12/tree/main/KPC-cF-main

KPCA-CAM: Visual Explainability of Deep Computer Vision Models using Kernel PCA

https://github.com/MindSpore-scientific-2/code-12/tree/main/group-normalization

Group Normalization

https://github.com/MindSpore-scientific-2/code-14/tree/main/groupvit

GroupViT: Semantic Segmentation Emerges from Text Supervision

https://github.com/MindSpore-scientific-2/code-14/tree/main/mamba

Mamba YOLO: A Simple Baseline for Object Detection with State Space Model

https://github.com/MindSpore-scientific-2/code-2/tree/main/SING

Single-Shot Refinement Neural Network for Object Detection

https://github.com/MindSpore-scientific-2/code-3/tree/main/Sub2Full-OCT-Denoising

OctFormer: Octree-based Transformers for 3D Point Clouds

https://github.com/MindSpore-scientific-2/code-3/tree/main/WaveCRN-master

Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning

https://github.com/MindSpore-scientific-2/code-4/tree/main/Deformable_Patch_Representation

Deformable ConvNets v2: More Deformable, Better Results

https://github.com/MindSpore-scientific-2/code-4/tree/main/PolyLoss

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/MindSpore-scientific-2/code-4/tree/main/fisheradversarial-mindspore

Robust and Accurate Object Detection via Adversarial Learning

https://github.com/MindSpore-scientific-2/code-4/tree/main/rand_conv-master

RandAugment: Practical automated data augmentation with a reduced search space

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