https://github.com/elmezianech/pretrained-image-classification

This is the First Project part of the AI-programming with Python Nanodegree by Udacity.
https://github.com/elmezianech/pretrained-image-classification

alexnet cnn resnet vgg

Last synced: about 8 hours ago
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This is the First Project part of the AI-programming with Python Nanodegree by Udacity.

• Host: GitHub
• URL: https://github.com/elmezianech/pretrained-image-classification
• Owner: elmezianech
• Created: 2024-07-05T18:35:27.000Z (4 months ago)
• Default Branch: master
• Last Pushed: 2024-07-05T18:57:06.000Z (4 months ago)
• Last Synced: 2024-07-06T00:29:23.956Z (4 months ago)
• Topics: alexnet, cnn, resnet, vgg
• Language: Python
• Homepage:
• Size: 8.42 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Use a Pre-trained Image Classifier to Identify Dog Breeds

This project was part of the AI-Programming with Python Nanodregree by Udacity.

## Principal Objectives

1. Correctly identify which pet images are of dogs (even if breed is misclassified) and which pet images aren't of dogs.

2. Correctly classify the breed of dog, for the images that are of dogs.

3. Determine which CNN model architecture (ResNet, AlexNet, or VGG), "best" achieve the objectives 1 and 2.

4. Consider the time resources required to best achieve objectives 1 and 2, and determine if an alternative solution would have given a "good enough" result, given the amount of time each of the algorithms take to run.

## Results Table

![image](https://github.com/elmezianech/Pretrained-Image-Classification/assets/120784838/e2d4dbaa-16ab-4850-840a-40ae45ed6c2e)

Given our results, the "best" model architecture is VGG. It outperformed both of the other architectures when considering both objectives 1 and 2. You will notice that ResNet did classify dog breeds better than AlexNet, but only VGG and AlexNet were able to classify "dogs" and "not-a-dog" at 100% accuracy. The model VGG was the one that was able to classify "dogs" and "not-a-dog" with 100% accuracy and had the best performance regarding breed classification with 93.3% accuracy.