https://github.com/kartikmehta8/udacity-aippn-project-1

This is my first project of Udacity's AI Programming with Python Nanodegree Program - Image Classification of different Dog Breeds.
https://github.com/kartikmehta8/udacity-aippn-project-1

cli cnn-classification machine-learning python

Last synced: about 2 months ago
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This is my first project of Udacity's AI Programming with Python Nanodegree Program - Image Classification of different Dog Breeds.

• Host: GitHub
• URL: https://github.com/kartikmehta8/udacity-aippn-project-1
• Owner: kartikmehta8
• Created: 2022-08-28T16:55:57.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-08-28T17:37:18.000Z (over 2 years ago)
• Last Synced: 2024-05-02T03:10:10.080Z (8 months ago)
• Topics: cli, cnn-classification, machine-learning, python
• Language: Python
• Homepage:
• Size: 8.58 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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## Image Classification for a City Dog Show

In this project, I have worked on an image classifier to identify dog breeds.

### Description:

Your city is hosting a citywide dog show and you have volunteered to help the organizing committee with contestant registration. Every participant that registers must submit an image of their dog along with biographical information about their dog. The registration system tags the images based upon the biographical information.

Some people are planning on registering pets that **aren’t actual dogs**.

You need to use an already developed Python classifier to make sure the participants are dogs.

**Note, you DO NOT need to create the classifier. It will be provided to you. You will need to apply the Python tools you just learned to USE the classifier.**

### Your Tasks:

- Using your Python skills, you will determine which image classification algorithm works the "best" on classifying images as "dogs" or "_not dogs_".

- Determine how well the "best" classification algorithm works on correctly identifying a dog's breed. If you are confused by the term _image classifier_ look at it simply as a tool that has an input and an output. The Input is an image. The output determines what the image depicts. (for example, a dog). Be mindful of the fact that image classifiers do not always categorize the images correctly.

- Time how long each algorithm takes to solve the classification problem. With computational tasks, there is often a trade-off between accuracy and runtime. The more accurate an algorithm, the higher the likelihood that it will take more time to run and use more computational resources to run.