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/a-nau/easy-image-scraping

Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics

https://github.com/a-nau/image-selection-and-cnn-training

Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection

https://github.com/a1302z/OktoberfestFoodDataset

Oktoberfest Food Dataset

https://github.com/a736875071/clip-vit-large-patch14

Learning Transferable Visual Models From Natural Language Supervision

https://github.com/aHapBean/PCP-MAE

Masked Autoencoders Are Scalable Vision Learners

https://github.com/aaboys/mobileformer

Mobile-Former: Bridging MobileNet and Transformer

https://github.com/aakashjhawar/traffic-sign-detection

YOLO9000: Better, Faster, Stronger

https://github.com/aakiraotok/yowov3

TOOD: Task-aligned One-stage Object Detection

https://github.com/aaron-wang-de/ab

YOLO9000: Better, Faster, Stronger

https://github.com/aaronfriedman6/MV3D_VoxelNet

Multi-View 3D Object Detection Network for Autonomous Driving

https://github.com/aashish2000/Parking_Management

Real-time object detection method based on improved YOLOv4-tiny

https://github.com/aasimsani/artificial_manga_panel_dataset

Object Detection for Comics using Manga109 Annotations

https://github.com/abahram77/mnistChallenge

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/abahram77/mnist_challenge

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/abaietto/neural_ode_classification

Deep Residual Learning for Image Recognition

https://github.com/abajaj945/Ship-Detection-using-Tensorflow

You Only Look Once: Unified, Real-Time Object Detection

https://github.com/abcxs/building_extraction

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/abcxs/maskrcnn-contest

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

https://github.com/abcxs/polyrnn

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/abdksyed/DETR

End-to-End Object Detection with Transformers

https://github.com/abdoelsayed2016/TNCR_Dataset

Deep learning for table detection and structure recognition: A survey

https://github.com/abdoelsayed2016/tncr

Deep learning for table detection and structure recognition: A survey

https://github.com/abdullahozer11/Segmentation-and-Classification-of-Objects-in-Point-Clouds

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

https://github.com/abhi1kumar/M3D-RPN

M3D-RPN: Monocular 3D Region Proposal Network for Object Detection

https://github.com/abhi1kumar/hrnet_pose_single_gpu

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/abhi2610/ohem

Training Region-based Object Detectors with Online Hard Example Mining

https://github.com/abhi4ssj/squeeze_and_excitation

Squeeze-and-Excitation Networks

https://github.com/abhigoku10/CenterPoint_PC

Center-based 3D Object Detection and Tracking

https://github.com/abhigoyal1997/CS-763-Project

Exploring Models and Data for Image Question Answering

https://github.com/abhileshborode/Behavorial-Clonng-Self-driving-cars

Deep Steering: Learning End-to-End Driving Model from Spatial and Temporal Visual Cues

https://github.com/abhimangalms/PlantDoctor

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/abhishek1411/event-rgb-fusion

https://github.com/abhoi/Keras-MnasNet

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/abhuse/polyloss-pytorch

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

https://github.com/abhuse/pytorch-efficientnet

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/abman23/pmnet

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

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