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cntk

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mmlspark
ttpro1995
ttpro1995 commented Nov 13, 2019

Version

com.microsoft.ml.spark:mmlspark_2.11:jar:0.18.1
spark= 2.4.3
scala=2.11.12

data (csv with header) https://gist.github.com/ttpro1995/69051647a256af912803c9a16040f43a

download data and save as csv file, put into folder /data/public/HIGGS/higgs.test.predictioncsv

val data = spark.read.option("header","true").option("inferSchema", "true").csv("/data/public/HIGGS

Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

  • Updated Jul 25, 2019
  • Jupyter Notebook

This POC is using CNTK 2.1 to train model for multiclass classification of images. Our model is able to recognize specific objects (i.e. toilet, tap, sink, bed, lamp, pillow) connected with picture types we are looking for. It plays a big role in a process which will be used to classify pictures from different hotels and determine whether it's a picture of bathroom, bedroom, hotel front, swimming pool, bar, etc.

  • Updated Feb 15, 2018
  • Python

This sample project shows off how to prepare and deploy to Azure Web Apps a simple Python web service with an image classifying model produced in CNTK (Cognitive Toolkit) using FasterRCNN

  • Updated Feb 5, 2018
  • Python

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