Papers with Code 아카이브

Object Detection

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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/code-implementation1/Code7/tree/main/ResNeXt

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/code-implementation1/Code7/tree/main/SE_ResNeXt50

Squeeze-and-Excitation Networks

https://github.com/code-implementation1/Code7/tree/main/rcnn

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

https://github.com/code-implementation1/Code7/tree/main/res2net

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/code-implementation1/Code7/tree/main/res2net_deeplabv3

Res2Net: A New Multi-scale Backbone Architecture

https://github.com/code-implementation1/Code7/tree/main/res2net_faster_rcnn

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

https://github.com/code-implementation1/Code7/tree/main/res2net_yolov3

YOLOX: Exceeding YOLO Series in 2021

https://github.com/code-implementation1/Code7/tree/main/resnetv2

Deep Residual Learning for Image Recognition

https://github.com/code-implementation1/Code7/tree/main/retinanet_resnet101

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/code-implementation1/Code7/tree/main/retinanet_resnet152

Stand-Alone Self-Attention in Vision Models

https://github.com/code-implementation1/Code7/tree/main/rfcn

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

https://github.com/code-implementation1/Code8/tree/main/SpineNet

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/code-implementation1/Code9/tree/main/MaskRCNN/maskrcnn_mobilenetv1

Mask R-CNN

https://github.com/code-implementation1/Code9/tree/main/MaskRCNN/maskrcnn_resnet50

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

https://github.com/code-implementation1/Code9/tree/main/PointNet2

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

https://github.com/code-implementation1/Code9/tree/main/RetinaFace_ResNet50

Region Proposal by Guided Anchoring

https://github.com/code-implementation1/Code9/tree/main/SSD

SSD: Single Shot MultiBox Detector

https://github.com/code-implementation1/Code9/tree/main/ShuffleNet

Slimmable Neural Networks

https://github.com/code-implementation1/Code9/tree/main/SwinTransformer

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/code-implementation1/Code9/tree/main/VIT

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

https://github.com/code-implementation1/Code9/tree/main/YOLOX

YOLOX: Exceeding YOLO Series in 2021

https://github.com/code-implementation1/Code9/tree/main/YOLOv3

YOLOX: Exceeding YOLO Series in 2021

https://github.com/code-implementation1/Code9/tree/main/YOLOv4

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/code-implementation1/Code9/tree/main/YOLOv5

YOLO5Face: Why Reinventing a Face Detector

https://github.com/cokowpublic/Nuclei-Segmentation

Mask R-CNN

https://github.com/coldsummerday/mmdetection-zhou

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/collectionslab/Omniscribe

Mask R-CNN

https://github.com/collectionslab/annotations-computervision

Mask R-CNN

https://github.com/collectionslab/book-annotation-classification

Mask R-CNN

https://github.com/computationalpathologygroup/hvit

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/conceptofmind/Crossformer-flax

CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention

https://github.com/conceptofmind/PiT-flax

Rethinking Spatial Dimensions of Vision Transformers

https://github.com/conceptofmind/Region-ViT-flax

RegionViT: Regional-to-Local Attention for Vision Transformers

https://github.com/condiff-dataset/ConDiff

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/conradry/copy-paste-aug

Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

https://github.com/conradry/pixpro-with-weights

Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning

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