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/contactnipun/Computer-Vision

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/coolbay/VSGN

Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

https://github.com/cooldawnant/infochartqa

OrionBench: A Benchmark for Chart and Human-Recognizable Object Detection in Infographics

https://github.com/coperception/coperception

V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction

https://github.com/coperception/double-m-quantification

Uncertainty Quantification of Collaborative Detection for Self-Driving

https://github.com/coreqode/HardNet

HarDNet: A Low Memory Traffic Network

https://github.com/cornell-zhang/allo-pldi24-artifact

Deep Residual Learning for Image Recognition

https://github.com/cosmic-cortex/pytorch-UNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/cpn/ShuffledVideoSorter

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/crowdai/crowdai-mapping-challenge-mask-rcnn

Mask R-CNN

https://github.com/cs-giung/course-dl-TP

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/cshizhe/maskrcnn_benchmark

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

https://github.com/csm-kr/Retinanet_pytorch

Focal Loss for Dense Object Detection

https://github.com/csmiler/tf-faster-rcnn-cpu

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

https://github.com/csuzhuzhuxia/GlassDetect

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/ctuning/ck-analytics

CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking

https://github.com/ctuning/ck-mlperf

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/cuifc1997/nuscene-study

nuScenes: A multimodal dataset for autonomous driving

https://github.com/cuihu1998/EfficientDet-d0

EfficientDet: Scalable and Efficient Object Detection

https://github.com/cuonga1cvp/yolov3_cuong

YOLOv3: An Incremental Improvement

https://github.com/cure-lab/magicdrive

MagicDrive: Street View Generation with Diverse 3D Geometry Control

https://github.com/cuzyoung/crossearth

MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining

https://github.com/cv516Buaa/tph-yolov5

TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios

https://github.com/cviviers/YOLOv5-6D-Pose

https://github.com/cwbabel/faster-cnn

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

https://github.com/cxmomo/Neighbor-Vote

Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting

https://github.com/cxt98/Densefusion-transparency

DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

https://github.com/cy-xu/spatially_aware_ai

kMaX-DeepLab: k-means Mask Transformer

https://github.com/cy0616/data_augment

Learning Data Augmentation Strategies for Object Detection

https://github.com/cyberpunk317/Action_detection

Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos

https://github.com/cycloneboy/pdf_table

Parsing Table Structures in the Wild

https://github.com/cyctrung/DPnet

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

https://github.com/cydiachen/offlinemmdet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/cyrilminaeff/MobileNet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/czarmanu/sentinel_lakeice

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/czero69/acomoeye-NN

GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

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