Corgy Porgy is an app that identify a Corgi. Yes, that's the only thing this app can do 😂 .
Inspired by one of Silicon Valley's episodes where the cast member Jian Yang created an app called Seefood, an app to identify whether the object is a hotdog or not hotdog 😂 😂 😂 .
Using the Tensorflow JS and corgi detection model generated using Teachable Machine to help the app identify a corgi. Made for fun at CorgiHacks.
#CorgiHacks #madewithTFJS
- Clone the repository
# copy the repo to your machine
git clone https://github.com/syauqy/corgyporgy.git- Start the project
# move to the project folder and install all dependencies
cd corgyporgy
yarn install- Run the project on your local machine
# run Gatsby
yarn develop- The project is live 🚀
Your project is live and running at http://localhost:8000
You can edit the core program at src/pages/app.js
I generated the corgi model using a Google's Teachable Machine. I'm using two classes for my model. Corgi and Not Corgi. Each class has several image samples. As you can see below.
The dog's image datasets are from Stanford Dogs Dataset
The model itself is not quite good since the corgi classes have fewer samples compare to the not corgi.
You can download and update the existing corgi, not corgi model here.
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Tensorflow JS - A Library for ML in JS.
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Object Detection model (Coco SSD) - A model detects objects defined in the COCO dataset, which is a large-scale object detection, segmentation, and captioning dataset.
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Teachable Machine - A fast, easy way to create machine learning models, by Google.
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Stanford Dogs Dataset - A dog breed dataset, contains images of 120 breeds of dogs from around the world.