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/cinastanbean/Pytorch-Multi-Task-Multi-class-Classification

Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/cioppaanthony/rt-sbs

Pyramid Scene Parsing Network

https://github.com/citbrains/darknet_yolov4

YOLOv3: An Incremental Improvement

https://github.com/cj-mclaughlin/segmentation_research

Deep Residual Learning for Image Recognition

https://github.com/cj4L/MST-python

https://github.com/cjf8899/KeepAugment_Pytorch

KeepAugment: A Simple Information-Preserving Data Augmentation Approach

https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations

Going Deeper with Convolutions

https://github.com/cjpurackal/m2det-tf

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

https://github.com/cjvargasc/YOLOv2

YOLO9000: Better, Faster, Stronger

https://github.com/ckyrkou/EmergencyNet

Searching for MobileNetV3

https://github.com/clairehester/face-mask-detector

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/clarencenhuang/dl-colorize

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/clasikman/darknet

YOLOv3: An Incremental Improvement

https://github.com/clavichord93/MENet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/clbeggs/MobileSal

MobileSal: Extremely Efficient RGB-D Salient Object Detection

https://github.com/clemkoa/faster-rcnn

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

https://github.com/clemkoa/fasterrcnn

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

https://github.com/clemkoa/u-net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/clemsgrs/hipt

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/cleverhans-lab/cleverhans

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/clip-italian/clip-italian

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/clive819/Modified-DETR

End-to-End Object Detection with Transformers

https://github.com/cloudnine148/PHC_2nd_SPC

YOLO9000: Better, Faster, Stronger

https://github.com/cmf3673/spotti

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/cmhungsteve/Awesome-Transformer-Attention

Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

https://github.com/cmitash/physim-dataset-generator

A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation

https://github.com/coconutzs/PointNet_zs

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/cocopambag/ResNet

Deep Residual Learning for Image Recognition

https://github.com/code-implementation1/Code1/tree/main/Deepsort

StrongSORT: Make DeepSORT Great Again

https://github.com/code-implementation1/Code1/tree/main/EfficientDet_d0

EfficientDet: Scalable and Efficient Object Detection

https://github.com/code-implementation1/Code1/tree/main/detr

DETRs with Collaborative Hybrid Assignments Training

https://github.com/code-implementation1/Code2

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/code-implementation1/Code3/tree/main/EGnet

https://github.com/code-implementation1/Code3/tree/main/FaceAttribute

Deep Residual Learning for Image Recognition

https://github.com/code-implementation1/Code3/tree/main/efficientnet-b0

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/code-implementation1/Code3/tree/main/erfnet

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