Inspiration

Sometimes it takes a lot of time to find good Instagram hashtags for your photo.

What it does

This app helps quickly find suitable hashtags for your picture.

How I built it

I build it with Android, TensorFlow Lite, MobileNet and Firebase ML vision image label model. I combine both predictions from MobileNet and Firebase ML model.

Challenges I ran into

ML models usually provide categories for images, but I wanted to provide popular hashtags. Thus, I had to collect on Instagram and different other resources, hashtags which are usually associated with the given category. For example, with category 'coffee', usually hashtags #love, #coffeetime, #cafe, #instagood,#coffeelover,#photography, #food, #coffeeshop are mentioned.

Accomplishments that I'm proud of

I collected a big database of hashtags which are mentioned in Instagram for different categories. My app is completely offline. It uses on device ML models.

What I learned

How to use TensorFlow and Firebase ML models on mobile devices.

What's next for EasyHash

Create better ML model for EasyHash. Provide for user on-device training the ML model, such that the recommendation of the hashtags will be customized for the user. Add suggestion of tags based on geolocation.

Built With

  • android
  • firebase-ml-vision
  • tensorflow
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