Papers with Code 아카이브

Object

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Replace the cat with a British Shorthair cat of the breed with bulging yellow eyes

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AGroupofProbiotocs/YOLO_V2_in_Tensorflow

YOLO9000: Better, Faster, Stronger

https://github.com/AI-liu/Complex-YOLO

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/AI-pha/dvs_tracker_brian2

Event-based attention and tracking on neuromorphic hardware

https://github.com/AKASH2907/bird-species-classification

https://github.com/AKASH2907/bird_species_classification

https://github.com/AP-EPFL/DA-segmentation-driven-pose

Segmentation-driven 6D Object Pose Estimation

https://github.com/AVAuco/ssd_people

SSD: Single Shot MultiBox Detector

https://github.com/Aarav-Patel/darknet

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Abdulrahman-Adel/Object-Detection

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

https://github.com/Abhi-899/YOLOV4-Custom-Object-Detection

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/Abhijeet2396/darknet

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/AbhinavJhanwar/object-detection-classification

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

https://github.com/Abhisek-/AlexNet

ImageNet Large Scale Visual Recognition Challenge

https://github.com/AceCoooool/DSS-pytorch

Deeply supervised salient object detection with short connections

https://github.com/ActiveVisionLab/ray-onet

CoReNet: Coherent 3D scene reconstruction from a single RGB image

https://github.com/Adam123wu/Edge-YOLO_algorithm

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/AdeelH/pytorch-multi-class-focal-loss

Focal Loss for Dense Object Detection

https://github.com/Adelaide-AI-Group/FCOS

FCOS: Fully Convolutional One-Stage Object Detection

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

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

https://github.com/Adren98/EczemaApp

Scalable Object Detection using Deep Neural Networks

https://github.com/AidanRocke/vertex_prediction

Annotating Object Instances with a Polygon-RNN

https://github.com/AishrithRao/robust-physical-attack

ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector

https://github.com/AjJordy/squeezeDet-master

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/AkashGanesan/PedestrianAttention

https://github.com/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection

Robust and Efficient Post-Processing for Video Object Detection (REPP)

https://github.com/AlexSzen/wwf_shark_id

Striving for Simplicity: The All Convolutional Net

https://github.com/Alexander-Whelan/Zeus

https://github.com/AliAbyaneh/Extracting-Image-from-EEG-signals

Deep Learning Human Mind for Automated Visual Classification

https://github.com/AliBahri94/SVWA_TTA

3D ShapeNets: A Deep Representation for Volumetric Shapes

https://github.com/AllenChen0958/homework3-GAN-Dissection

GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

https://github.com/AllenPeng0209/deeproute_competition

Exploring Data Augmentation for Multi-Modality 3D Object Detection

https://github.com/AlongRide/CenterNet_anchor_free

Objects as Points

https://github.com/AlphaGoMK/arxiv-explorer

Plug & Play Convolutional Regression Tracker for Video Object Detection

https://github.com/AlphaJia/pytorch-faster-rcnn

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

https://github.com/AlphaJia/tf-faster-rcnn

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

https://github.com/AlvesRobot/Deep-Learning-on-Point-Cloud-for-3D-Classification-and-Segmentation

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

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