Metadata-Version: 2.1
Name: image_embeddings
Version: 1.1.2
Summary: # image_embeddings
Home-page: UNKNOWN
Author: Romain Beaumont
License: UNKNOWN
Description: # image_embeddings
        [![pypi](https://img.shields.io/pypi/v/image_embeddings.svg)](https://pypi.python.org/pypi/image_embeddings)
        [![ci](https://github.com/rom1504/image_embeddings/workflows/Continuous%20integration/badge.svg)](https://github.com/rom1504/image_embeddings/actions?query=workflow%3A%22Continuous+integration%22)
        
        
        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
        
        ## Simple Install
        
        Run `pip install image_embeddings`
        
        ## Example workflow
        
        1. run `image_embeddings save_examples_to_folder --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
        
        ## API
        
        ### image_embeddings.downloader.save_examples_to_folder(output_folder, dataset="tf_flowers")
        
        Save https://www.tensorflow.org/datasets/catalog/tf_flowers to folder
        Also works with other tf datasets
        
        ## Advanced 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
        
        * [x] 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
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 5 - Production/Stable
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
