segementation

An implementation of《Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks》

Fine-tune Facebook's DETR (DEtection TRansformer) on Colaboratory.

MATLAB implementation to segment breast lesions in ultrasound images (ICIAR 2016)

u net (remote sensing image segmentation)

🚥 SegmentSwitchTab

类似简书首页、网易云音乐、UC浏览器新闻界面的顶部滚动导航条

🏥 医学图像作业：图像配准论文阅读；眼底血管分割实验

HVSMR 2016: MICCAI Workshop on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease

Recognize scanned sheets and convert them into Excel sheet

My Data Science Degree Capstone Project

My take on card localization and classification

A deep learning approach to fight COVID virus. This model uses CNN with transfer learning to detect if a person is infected with COVID by looking at the lung X-Ray and further it segments the infected region of lungs producing a mask using U-Net

Predict the type and localise the defect using Image Segmentation

A collection of scripts to create an audio data-set with energy based segmentation.

Was hacking around with the KITTI dataset to create an encoder-decoder CNN for road segementation.

Use case: A portable and automated workflow, to be run on demand. Functionalities: Workflow extracted and read data from a given SQL server instance. Prompt input for location of Excel file to write on. Segmentation and aggregations, given conditions. Ran a developed macro in VBA, verifying the workflow output.

🏥 java 8 swing application to view computed tomography scanned medical images

The purpose of this repository is to provide good insight to the Pytorch practitioner, especially for the newbie.

Implementation of U_Net architecture for medical image segmentation purpose.

# Using technology of computer vision to classify clouds.

Baidu Tieba spider, word segmentation and wordcloud

✨Image Segmentation Detection Competition(AI BoostCamp Proejct) ✅ COCO dataset ✅ LB: 9등, 4등

Bank Customer Segmentation and Insurance Claim Prediction - The project involved drawing inferences from 2 case studies, namely - Bank Marketing & Insurance. The concepts of Clustering, CART, Random Forest, Artificial Neural Network are used to draw inferences from these case studies. Various performance metrics have been used to validate the performance of predictions on Test & Train sets. Skills and Tools Clustering, CART, Random Forest, Artificial Neural Networks.

Marketing in a Digital World - Implications of Internet Core Trends for TV Advertising

DeepLab v3+ model in PyTorch. Support different backbones.

This is a data analysis portfolio project that will allow understand to performance of customers based on segmentation of mall customers

A Predictive Analysis of H1B Dataset using both supervised and unsupervised learning

GIS - Master level course in Physical Geography at Marburg University