https://github.com/vpanjeta/hotdog-or-not-hotdog

Implementation of Hot Dog or not app from Silicon Valley (CNN to identify if the given picture is a hot dog or not)
https://github.com/vpanjeta/hotdog-or-not-hotdog

cnn convolutional-neural-networks deep-learning deep-neural-networks deeplearning hot-dogs hotdog image-classification image-classifier image-recognition inception not-hotdog picture python silicon silicon-valley tensorflow tensorflow-tutorials valley

Last synced: 8 days ago
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Implementation of Hot Dog or not app from Silicon Valley (CNN to identify if the given picture is a hot dog or not)

• Host: GitHub
• URL: https://github.com/vpanjeta/hotdog-or-not-hotdog
• Owner: VPanjeta
• Created: 2017-06-05T08:46:48.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-06-05T09:19:54.000Z (over 7 years ago)
• Last Synced: 2024-11-12T17:03:57.169Z (8 days ago)
• Topics: cnn, convolutional-neural-networks, deep-learning, deep-neural-networks, deeplearning, hot-dogs, hotdog, image-classification, image-classifier, image-recognition, inception, not-hotdog, picture, python, silicon, silicon-valley, tensorflow, tensorflow-tutorials, valley
• Language: Python
• Homepage:
• Size: 77.3 MB
• Stars: 65
• Watchers: 2
• Forks: 12
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# hotdog-or-not-hotdog

Implementation of Hot Dog or not app from Silicon Valley (CNN to identify if the given picture is a hot dog or not)

I used Google's Inception (2015) model. Inception is a deep convolutional neural network built for classifying real world images of thousand category. 


Retraining done by replacing last layer of Inception model. 


Training has been done using around 300 images so the accuracy for certain images might be low. 


## Getting started

1. Clone and run bash on this repository. (Ensure that you have TensorFlow installed.)

2. Then run `python label_dog.py test/bottle.jpg`

![bottle](test/bottle.jpg)

3. Wait for the model's prediction.

4. Here's the result you should expect if things work correctly:

```

not hot dog          : 0.93821

hot dog              : 0.06179

```

5. For hot dog run `python label_dog.py test/hotdog.jpg`

![hotdog](test/hotdog.jpg)

6. The result comes out as 

```

hot dog              : 0.99862

not hot dog          : 0.00138

```