Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/gbnk0/simple-image-classifier

Simple image classifier microservice using tensorflow and sanic
https://github.com/gbnk0/simple-image-classifier

api classifier image label learning machine sanic simple tensorflow

Last synced: about 1 month ago
JSON representation

Simple image classifier microservice using tensorflow and sanic

Awesome Lists containing this project

README

        

# simple-image-classifier
Simple image classifier microservice using tensorflow

![pipeline status](https://travis-ci.org/gbnk0/simple-image-classifier.svg?branch=master)
![dockerbuild](https://img.shields.io/docker/automated/jrottenberg/ffmpeg.svg)
[![](https://images.microbadger.com/badges/image/gbnk0/simple-image-classifier.svg)](https://microbadger.com/images/gbnk0/simple-image-classifier)
[![](https://images.microbadger.com/badges/version/gbnk0/simple-image-classifier.svg)](https://microbadger.com/images/gbnk0/simple-image-classifier)
[![](https://images.microbadger.com/badges/commit/gbnk0/simple-image-classifier.svg)](https://microbadger.com/images/gbnk0/simple-image-classifier)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/2541d5b6ddaa48c8a5d834ea03649372)](https://www.codacy.com/app/gbnk0/simple-image-classifier?utm_source=github.com&utm_medium=referral&utm_content=gbnk0/simple-image-classifier&utm_campaign=Badge_Grade)
![python_version](https://img.shields.io/badge/python-3.5%2C3.6-blue.svg)

#### PREREQUISITES FOR LOCAL RUN
- A linux system
- python >= 3.5

#### QUICKSTART GUIDE

##### RUN WITH DOCKER
```
docker run -p8080:8080 gbnk0/simple-image-classifier:latest
```

##### LOCAL RUN WITH PYTHON
Go to the app folder:
```
cd app/
pip3 install -r requirements.txt
python3 app.py
```

It will listen on localhost:8080

#### EXAMPLES:
```
cd example-lib/
edit the example.py file with your set of pictures
python3 example.py
```

#### POPULATING DATASET

When you successfully created a dataset using the /datasets endpoint:
One method for populating the new dataset is to copy all your categories folders (dog, cat, fish, people) to the data/{datasetName}/labels/ folder.

#### API DOCUMENTATION:

[![Run in Postman](https://run.pstmn.io/button.svg)](https://documenter.getpostman.com/view/3045659/RWMHM7ir)