image-embeddings 1.0.0

# image_embeddings

image_embeddings

Using efficientnet to provide embeddings for retrieval.

Why this repo ? Embeddings are a widely used technique that is well known in scientific circles. But it seems to be underused and not very well known for most engineers. I want to show how easy it is to represent things as embeddings, and how many application this unlocks.

Workflow

1. download some pictures
2. run inference on them to get embeddings
3. simple knn example, to understand what's the point : click on some pictures and see KNN

Example workflow

1. run python image_embeddings/cli/tf_datasets_to_files.py --output_folder=tf_flower_images, this will retrieve the image files from https://www.tensorflow.org/datasets/catalog/tf_flowers (but you can also pick any other dataset)
2. run the inference
3. run the knn

Installation

Prerequisites

Make sure you use python>=3.6 and an up-to-date version of pip and setuptools

python --version
pip install -U pip setuptools

It is recommended to install image_embeddings in a new virtual environment. For example

python3 -m venv image_embeddings_env
source image_embeddings_env/bin/activate
pip install -U pip setuptools
pip install image_embeddings

Using Pip

pip install image_embeddings

From Source

First, clone the image_embeddings repo on your local machine with

git clone https://github.com/rom1504/image_embeddings.git
cd image_embeddings
make install

To install development tools and test requirements, run

make install-dev

Test

To run unit tests in your current environment, run

make test

To run lint + unit tests in a fresh virtual environment, run

make venv-lint-test

Lint

To run black --check:

make lint

To auto-format the code using black

make black

Tasks

• simple downloader in python
• simple inference in python using https://github.com/qubvel/efficientnet
• build python basic knn example using https://github.com/facebookresearch/faiss
• build basic ui using lit element and some brute force knn to show what it does, put in github pages
• use to illustrate embeddings blogpost