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/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

https://github.com/2023-MindSpore-4/Code2/tree/main/darknet53

RealNet: Combining Optimized Object Detection with Information Fusion Depth Estimation Co-Design Method on IoT

https://github.com/2023-MindSpore-4/Code3/tree/main/Deepsort

StrongSORT: Make DeepSORT Great Again

https://github.com/2023-MindSpore-4/Code3/tree/main/E-NET

https://github.com/2023-MindSpore-4/Code3/tree/main/EfficientDet_d0

EfficientDet: Scalable and Efficient Object Detection

https://github.com/2023-MindSpore-4/Code3/tree/main/detr

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

https://github.com/2023-MindSpore-4/Code3/tree/main/efficientnet-b0

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/2023-MindSpore-4/Code4/tree/main/GLNet-TCYB2022-MindSpore

Global-and-Local Collaborative Learning for Co-Salient Object Detection

https://github.com/2023-MindSpore-4/Code4/tree/main/tnet_mindspore

Does Thermal Really Always Matter for RGB-T Salient Object Detection?

https://github.com/2023-MindSpore-4/Code4/tree/main/wslnet

A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels

https://github.com/2023-MindSpore-4/Code6/tree/main/PVAnet

PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

https://github.com/2023-MindSpore-4/Code6/tree/main/RefineDet

FSSD: Feature Fusion Single Shot Multibox Detector

https://github.com/2023-MindSpore-4/Code6/tree/main/ResNeSt50

ResNeSt: Split-Attention Networks

https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_mobilenetV2

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_mobilenetV2_FPNlite

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet34

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet_34

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-4/Code8/tree/main/3D_DenseNet

DenseNet: Implementing Efficient ConvNet Descriptor Pyramids

https://github.com/2023-MindSpore-4/Code8/tree/main/CBAM

CBAM: Convolutional Block Attention Module

https://github.com/2023-MindSpore-4/Code8/tree/main/augvit

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

https://github.com/2023-MindSpore-4/Code8/tree/main/centerface

CenterNet: Keypoint Triplets for Object Detection

https://github.com/2023-MindSpore-4/Code8/tree/main/centernet_det

CenterNet: Keypoint Triplets for Object Detection

https://github.com/2023-MindSpore-4/Code8/tree/main/centernet_resnet101

CenterNet: Keypoint Triplets for Object Detection

https://github.com/2023-MindSpore-4/Code8/tree/main/convnext

A ConvNet for the 2020s

https://github.com/2023-MindSpore-4/Code9/tree/main/MaskRCNN

Mask R-CNN

https://github.com/2023-MindSpore-4/Code9/tree/main/MobileNet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/2023-MindSpore-4/Code9/tree/main/PointNet2

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/2023-MindSpore-4/Code9/tree/main/ResNet

Deep Residual Learning for Image Recognition

https://github.com/2023-MindSpore-4/Code9/tree/main/RetinaNet

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/2023-MindSpore-4/Code9/tree/main/ShuffleNet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/2023-MindSpore-4/Code9/tree/main/SwinTransformer

Self-Supervised Learning with Swin Transformers

https://github.com/2023-MindSpore-4/Code9/tree/main/YOLOv3

YOLOv3: An Incremental Improvement

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

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