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/Bigwode/rebar-detection-competition

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

https://github.com/BillyGun27/mobilenet_model

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021

FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding

https://github.com/BingfengYan/DS_OCSORT

Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking

https://github.com/BingkAI-B21CAP0161/C-Mask-Machine-Learning

Mask R-CNN

https://github.com/BioMedIA-MBZUAI/Echocardiography-Assess

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/BioWar/Satellite-Image-Segmentation-using-Deep-Learning-for-Deforestation-Detection

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/BlackAngel1111/Fast-RCNN

Fast R-CNN

https://github.com/BlackAngel1111/Yolo3_Pytorch

YOLOv3: An Incremental Improvement

https://github.com/Blaizzy/BiSeNet-Implementation

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

https://github.com/Bo396543018/picodet_repro

Instances as Queries

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

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/Boese0601/DUT_AI_2020_mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/BonJunKu/ScooterHelmetDetector

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

https://github.com/Booomshaker/SparseRCNNWSL

Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

https://github.com/Bribak/DeepConnection

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

https://github.com/BrilliantYuKaimin/XCiT-PaddlePaddle

XCiT: Cross-Covariance Image Transformers

https://github.com/Brunogomes97/Imdb

Random Erasing Data Augmentation

https://github.com/BryanPlummer/Two_branch_network

Revisiting Image-Language Networks for Open-ended Phrase Detection

https://github.com/BryanPlummer/cite

Revisiting Image-Language Networks for Open-ended Phrase Detection

https://github.com/BupyeongHealer/Mask_RCNN_tf_2.x

Mask R-CNN

https://github.com/Burf/EfficientNet-Lite-Tensorflow2

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/Burf/HRNetV2-OCR-Tensorflow2

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/Burf/ResNeSt-Tensorflow2

ResNeSt: Split-Attention Networks

https://github.com/Burf/ResNet-Tensorflow2

Deep Residual Learning for Image Recognition

https://github.com/Burf/SwinTransformer-Tensorflow2

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/Burf/tfdetection

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

https://github.com/Burton2000/semantic_segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/C677/Machine-learning

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

https://github.com/CAMMA-public/ai4surgery

EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos

https://github.com/CAPTAIN-WHU/DOTA_devkit

DOTA: A Large-scale Dataset for Object Detection in Aerial Images

https://github.com/CASIA-IVA-Lab/ISP-reID

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/CMU-Perceptual-Computing-Lab/openpose

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/CRIGIM/darknet

YOLOv3: An Incremental Improvement

https://github.com/CS-GangXu/TMNet

Deformable ConvNets v2: More Deformable, Better Results

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