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/Sungman-Cho/Propagate-Yourself-Pytorch

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

https://github.com/SuperKam91/gns

Multimodal nested sampling: an efficient and robust alternative to MCMC methods for astronomical data analysis

https://github.com/SupreethN/Hand-CNN

Contextual Attention for Hand Detection in the Wild

https://github.com/Sushma07/dancedarknet

YOLO9000: Better, Faster, Stronger

https://github.com/Suvoo/U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/SweetyTian/efficientdet

EfficientDet: Scalable and Efficient Object Detection

https://github.com/SyBorg91/pose-estimation-detection

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Syarujianai/deeplab-commented

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/Syavaprd/mobilenet_v3

Searching for MobileNetV3

https://github.com/SyedUmaidAhmed/Pose-Detection-Sitting-Standing

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/SysCV/tet

Quasi-Dense Similarity Learning for Multiple Object Tracking

https://github.com/TENNLab-UTK/dbscan

DOTIE -- Detecting Objects through Temporal Isolation of Events using a Spiking Architecture

https://github.com/THU17cyz/3DIoUMatch-PVRCNN

3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection

https://github.com/TKGgunter/yolov4_tiny_rs

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/TRAILab/ST-SLidR

OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning

https://github.com/TTMRonald/MobileNet_Zoo

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Tajamul21/Detection-Classification-and-Semantic_Segmentation-of-apples

MinneApple: A Benchmark Dataset for Apple Detection and Segmentation

https://github.com/TamirShasha/Mars_yolo

YOLOv3: An Incremental Improvement

https://github.com/TangDL/DCN

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

https://github.com/TangDL/mmdetection

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/TangShengqin/ATSS-Pytorch

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

https://github.com/TangShengqin/DynamicRCNN-mmdet

Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training

https://github.com/TanyaChutani/SSD-Tensorflow

SSD: Single Shot MultiBox Detector

https://github.com/TanyaChutani/SSD-Tensorflow2.0

A Solution to Product detection in Densely Packed Scenes

https://github.com/TanyaChutani/Squeeze-and-Excitation-Networks-ResNet50-Tf2.0

Squeeze-and-Excitation Networks

https://github.com/TanyaChutani/U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/Tashnim-Chowdhury/FloodNet-Challenge-EARTHVISION2021

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

https://github.com/TeamA2020/Practice

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

https://github.com/Techyee/darknet_resource

YOLO9000: Better, Faster, Stronger

https://github.com/TejasBajania/Mtech_pro

Mask R-CNN

https://github.com/TejasBajania/Mtech_thesis_project

Mask R-CNN

https://github.com/Tez01/SegNet-Keras-Implementation

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/ThanasisMattas/smartflow

Deep Residual Learning for Image Recognition

https://github.com/ThanhDinhDat/center_net_points

Objects as Points

https://github.com/The-AI-Summer/self_attention

Stand-Alone Self-Attention in Vision Models

https://github.com/The-GAN-g/DeblurGAN

DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks

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