Cortex: Deep Data Analysis Platform

Description

Problem

​

• Deep neural networks can solve wide variety of machine learning problems well

• Deep neural networks used in industry applications usually work the best when they are trained using supervised learning given that:

  • there is a lot of data available,

  • the training data is from the same distribution as the data from the production environment and 

  • the labels and data are of a high quality

• Large amounts of data is available on and outside the Internet, but it is not useful for building machine learning solutions in raw format

Solution​

​​

• Solving the process of:

  • Collecting large sets of data at the business process level

  • Preparing and labeling the data for use in training/evaluation processes of deep neural networks

  • Quality assurance of collected data and labels

• Using multidisciplinary approach (technical, social, ethical, legal, ...)

• Automating the process

• Note - currently focused only around the image data for three computer vision tasks:

  • image classification, 

  • object detection and 

  • object segmentation

Data Collection, Labeling and QA Process

Mission Statement

​

To create the biggest high quality labeled dataset for building machine learning models.

Papers​

Tools

Computer Vision Annotation Tool

Click on the icon!

Deep Data Analysis API

​

Deep Data Analysis API allows users to upload image and/or video files (audio and text files soon) and receive deep data analysis. Deep Data Analysis ID is given to each file by using which user can get associated analysis. More modules for different types of analyses will be added periodically.

​

cURL command:

curl --location --request POST 'https://api.piculjantechnologies.ai/upload/' --header 'Content-Type: application/json' --data-raw '{"url_or_id": "https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg"}'

​

Python code snippet:

import requests
import json

url = "https://api.piculjantechnologies.ai/upload/"

payload={'url_or_id': 'https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg'}
headers = {
  'Content-Type': 'application/json'
}

response = requests.request("POST", url, headers=headers, data=json.dumps(payload))

print(response.text)
 

Output:

{
   "_id":"PT::62d19d20d659408193861108",
   "url":"https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Cat_November_2010-1a.jpg/1200px-Cat_November_2010-1a.jpg",
   "datetime":"2022-07-15 17:00:16.330426",
   "hash":"d0ad50c952a9a153fd7b0f9765dec721f24c814dbe2ca1010d0b28f0f74a2def",
   "type":"image",
   "height":416,
   "width":416,
   "object_analysis":[
      [
         {
            "classname":"cat",
            "conf":0.99759441614151,
            "cls_conf":0.9897450804710388,
            "x1":118,
            "y1":52,
            "x2":335,
            "y2":399
         },
         {
            "classname":"bed",
            "conf":0.8249074816703796,
            "cls_conf":0.6781684756278992,
            "x1":61,
            "y1":27,
            "x2":341,
            "y2":401
         }
      ]
   ],
   "face_analysis":[
      [
         
      ]
   ],
   "pose_estimation_analysis":[
      [
         
      ]
   ]
}