Classify Dogs and Cats with Google AutoML

This is my first project with Google Auto ML and I was very curious to try it out because I have seen a lot of interesting post here on Medium about it. For example I suggest you to read this very interesting post from Sriram Gopal where he explains all the steps to approach a similar project using Google AutoML

I decided to use the Dogs vs. Cats dataset from Kaggle. The goal is classify whether images contain either a dog or a cat

Let’s start by using the CRISP-DM Process (Cross Industry Process for Data Mining):

  1. Business Understanding
  2. Data Understanding
  3. Prepare Data
  4. Data Modeling
  5. Evaluate the Results
  6. Deploy

Business Understanding

Image classification is a pretty common task nowadays and it consists in taking an image and some classes as input and outputting a probability that the input image belongs to one or more of the given classes

A part from the main goal of the projects I wanted to try Google Auto ML

Data Understanding

Data provided by Kaggle:

  • test: folder with 12500 images of dogs and cats to be used as test dataset
  • train: folder with 25000 images of dogs and cats with the label in the image name i.e cat.0.jpg, dog.0.jpg
  • sampleSubmission.csv: csv file to use as reference for the submission when participating at the competition

The images are RGB and have different dimensions

The structure of the dataset from Kaggle is pretty different from the one expected from Google AutoML which consists in one folder per label with the images inside

Prepare Data and Data Modeling

The labeling of the images was already done by Kaggle as already said and the selection of the model and its tuning will be done by Google AutoML Vision

To use Google AutoML Vision:

  1. Create a Google Cloud Platform account
  2. Create a new project
  3. Enable AutoML API
  4. Create a Service Account
  5. Create a bucket
  6. Create a .csv file to map the images of the dataset
  7. Upload the images into the bucket
  8. Create a dataset and upload the images
  9. Train the model
  10. Deploy the model

So we just have to provide the data to Google AutoML Vision in the expected format. To let Google AutoML Vision use the data:

  1. Separate the images from the Kaggle **train **folder into **dog **and **cat **folders
  2. Upload the **dog **and **cat **folders into a bucket in Google Cloud Storage
  3. Create a csv file to map the images of the dataset in the bucket

The dataset has been automatically splittend in train, validation and test datasets. It is also possible to specify in the mapping csv file TRAIN, VALIDATION, TEST or UNASSIGNED for each image to change the ratio between how many images are used for train, validation and test

#data-science #google-automl #classification #data analysis

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Classify Dogs and Cats with Google AutoML
Jon  Gislason

Jon Gislason

1619247660

Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

What Are Google Compute Engine ? - Explained

What Are Google Compute Engine ? - Explained

The Google computer engine exchanges a large number of scalable virtual machines to serve as clusters used for that purpose. GCE can be managed through a RESTful API, command line interface, or web console. The computing engine is serviced for a minimum of 10-minutes per use. There is no up or front fee or time commitment. GCE competes with Amazon’s Elastic Compute Cloud (EC2) and Microsoft Azure.

https://www.mrdeluofficial.com/2020/08/what-are-google-compute-engine-explained.html

#google compute engine #google compute engine tutorial #google app engine #google cloud console #google cloud storage #google compute engine documentation

Classify Dogs and Cats with Google AutoML

This is my first project with Google Auto ML and I was very curious to try it out because I have seen a lot of interesting post here on Medium about it. For example I suggest you to read this very interesting post from Sriram Gopal where he explains all the steps to approach a similar project using Google AutoML

I decided to use the Dogs vs. Cats dataset from Kaggle. The goal is classify whether images contain either a dog or a cat

Let’s start by using the CRISP-DM Process (Cross Industry Process for Data Mining):

  1. Business Understanding
  2. Data Understanding
  3. Prepare Data
  4. Data Modeling
  5. Evaluate the Results
  6. Deploy

Business Understanding

Image classification is a pretty common task nowadays and it consists in taking an image and some classes as input and outputting a probability that the input image belongs to one or more of the given classes

A part from the main goal of the projects I wanted to try Google Auto ML

Data Understanding

Data provided by Kaggle:

  • test: folder with 12500 images of dogs and cats to be used as test dataset
  • train: folder with 25000 images of dogs and cats with the label in the image name i.e cat.0.jpg, dog.0.jpg
  • sampleSubmission.csv: csv file to use as reference for the submission when participating at the competition

The images are RGB and have different dimensions

The structure of the dataset from Kaggle is pretty different from the one expected from Google AutoML which consists in one folder per label with the images inside

Prepare Data and Data Modeling

The labeling of the images was already done by Kaggle as already said and the selection of the model and its tuning will be done by Google AutoML Vision

To use Google AutoML Vision:

  1. Create a Google Cloud Platform account
  2. Create a new project
  3. Enable AutoML API
  4. Create a Service Account
  5. Create a bucket
  6. Create a .csv file to map the images of the dataset
  7. Upload the images into the bucket
  8. Create a dataset and upload the images
  9. Train the model
  10. Deploy the model

So we just have to provide the data to Google AutoML Vision in the expected format. To let Google AutoML Vision use the data:

  1. Separate the images from the Kaggle **train **folder into **dog **and **cat **folders
  2. Upload the **dog **and **cat **folders into a bucket in Google Cloud Storage
  3. Create a csv file to map the images of the dataset in the bucket

The dataset has been automatically splittend in train, validation and test datasets. It is also possible to specify in the mapping csv file TRAIN, VALIDATION, TEST or UNASSIGNED for each image to change the ratio between how many images are used for train, validation and test

#data-science #google-automl #classification #data analysis

Embedding your <image> in google colab <markdown>

This article is a quick guide to help you embed images in google colab markdown without mounting your google drive!

Image for post

Just a quick intro to google colab

Google colab is a cloud service that offers FREE python notebook environments to developers and learners, along with FREE GPU and TPU. Users can write and execute Python code in the browser itself without any pre-configuration. It offers two types of cells: text and code. The ‘code’ cells act like code editor, coding and execution in done this block. The ‘text’ cells are used to embed textual description/explanation along with code, it is formatted using a simple markup language called ‘markdown’.

Embedding Images in markdown

If you are a regular colab user, like me, using markdown to add additional details to your code will be your habit too! While working on colab, I tried to embed images along with text in markdown, but it took me almost an hour to figure out the way to do it. So here is an easy guide that will help you.

STEP 1:

The first step is to get the image into your google drive. So upload all the images you want to embed in markdown in your google drive.

Image for post

Step 2:

Google Drive gives you the option to share the image via a sharable link. Right-click your image and you will find an option to get a sharable link.

Image for post

On selecting ‘Get shareable link’, Google will create and display sharable link for the particular image.

#google-cloud-platform #google-collaboratory #google-colaboratory #google-cloud #google-colab #cloud

Jeromy  Lowe

Jeromy Lowe

1597776900

Top Google AI, Machine Learning Tools for Everyone

_“We want to use AI to augment the abilities of people, to enable us to accomplish more and to allow us to spend more time on our creative endeavors.” _-- Jeff Dean, Google Senior Fellow

Calling Google just a search giant would be an understatement with how quickly it grew from a mere search engine to a driving force behind innovations in several key IT sectors. Over the past couple of years, Google has planted its roots into almost everything digital, be it consumer electronics such as smartphones, tablets, laptops, its underlying software such as Android and Chrome OS or the smart software backed by Google’s AI.

Google has been actively innovating in the smart software industry. Backed by its expertise in search and analytical data acquired over the years have helped Google create various tools like TensorFlowML KitCloud AI, and many more for enthusiasts and beginners alike who are trying to understand the capabilities of AI.

#ai #automl #data science platforms #datasets #google #google cloud #google colab #machine learning #tensorflow