Identify good picture collection to download about image_embeddings HOT 8 CLOSED

ideas :

• download picture from google image
• extract pictures from youtube videos
• use image net (but that's too easy)
• find some interesting picture dataset in kaggle
• use a crawler to download images from website :
  • an housing website
  • animal pictures such as https://pixabay.com/images/search/animal/
  • wikipedia pictures

problem with 1M pictures is it also means hosting it somewhere. Might be good to keep the url so it can be used for visualization ?

from image_embeddings.

might be reasonable to start with a simple example with 100 pictures, use that as basic example and expand to the more complete example afterwards

from image_embeddings.

https://lionbridge.ai/datasets/top-10-image-classification-datasets-for-machine-learning/
https://www.tensorflow.org/datasets/catalog/sun397

from image_embeddings.

plan :

• use https://www.tensorflow.org/datasets/catalog/sun397
• download it
• select 100 decent images of it
• run inference
• commit the 100 images and 100 embeddings
• build basic python knn and js knn
• then run fast inference on all 100k images
• try python knn on them
• put screenshots in readme
• write blog post

from image_embeddings.

using tf_flowers instead as sun397 is too big for a simple example

from image_embeddings.

bootstrapped in https://github.com/rom1504/image-embeddings/blob/master/ImageEmb.ipynb
Next steps :

• put that in clean .py files
• create setup.py / requirements.txt
• create clean README
• consider putting on pypi
• screenshot in readme
• js knn
• blogpost

Other things to consider :

• provide other ways to get pictures (add a simple resizer that work for generic picture folder)

from image_embeddings.

Remaining :

• inference in .py (in api and cli)
• knn in .py (in api and cli)
• get screenshots for readme and blogpost
• js knn

from image_embeddings.

done
opening issues for the rest (js knn and blogpost)

from image_embeddings.