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/Rishit-dagli/Greenathon-Plant-AI

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Ritik-Sharma38/YoloV3_training_Hands

YOLO9000: Better, Faster, Stronger

https://github.com/RituYadav92/Image-segmentation

Pyramid Scene Parsing Network

https://github.com/RoadoneP/aloha

U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection

https://github.com/RobbertBrand/Yolo-Tensorflow-Implementation

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

https://github.com/RobertHong1992/Resnest

ResNeSt: Split-Attention Networks

https://github.com/Robinatp/Deeplab_Tensorflow

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/RobotMobile/cv-deep-learning-paper-review

Deep Residual Learning for Image Recognition

https://github.com/RocketFlash/CAP_augmentation

Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

https://github.com/RogerQi/pascal-5i

Fully Convolutional Networks for Semantic Segmentation

https://github.com/Rohed/ml-1

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

https://github.com/RooKichenn/CEFPN

CE-FPN: Enhancing Channel Information for Object Detection

https://github.com/RooKichenn/pytorch-MOAT

MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models

https://github.com/RooKichenn/pytorch-MaxViT

MaxViT: Multi-Axis Vision Transformer

https://github.com/Rosie-Brigham/sesmeg

Pyramid Scene Parsing Network

https://github.com/Roypic/Attention_Code

Squeeze-and-Excitation Networks

https://github.com/RuiZhou-cn/U3HS

Segmenting Known Objects and Unseen Unknowns without Prior Knowledge

https://github.com/RuiminChen/GIou_loss_caffe

Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

https://github.com/RuiminChen/GIouloss_CIouloss_caffe

Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

https://github.com/RuiminChen/centernet

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/RunzhaoHuang/DeepSort_YOLOV5_OnScreen

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/RuoyuChen10/objectdetection-saliency-maps

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

https://github.com/RyanHTR/PyTorch-Encoding

Context Encoding for Semantic Segmentation

https://github.com/RyanXingQL/RBQE

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/SAIGANESH-Collab/DARKNET

YOLOv3: An Incremental Improvement

https://github.com/SAIGANESH-Collab/YOLO-AQUA

YOLOv3: An Incremental Improvement

https://github.com/SANKHA1/Vehicle-Detection

Deep Residual Learning for Image Recognition

https://github.com/SDL-GuoZonghao/BeyondBoundingBox

https://github.com/SDMrFeng/quiz-w10-fcn

Fully Convolutional Networks for Semantic Segmentation

https://github.com/SEU-SRTP-GROUP/frustum-pointnet-original

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/SH-Tan/RangeRcnn-backbone

RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation

https://github.com/SHI-Labs/Decoupled-Classification-Refinement

Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection

https://github.com/SHU-FLYMAN/Yolov1-TensorFlow

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

https://github.com/SK124/Semantic-Segmentaion-Using-UNets

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/SSN15/Behavioral-Cloning--Implementaion-of-Autonomous-car-using-deep-learning

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

https://github.com/SSN15/Traffic-sign-recognition-using-deep-learning-and-computer-vision

Going Deeper with Convolutions

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