Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
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: 3 months ago
JSON representation
Simple image classifier microservice using tensorflow and sanic
- Host: GitHub
- URL: https://github.com/gbnk0/simple-image-classifier
- Owner: gbnk0
- License: mit
- Created: 2018-07-18T20:49:51.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-02-04T14:02:47.000Z (about 6 years ago)
- Last Synced: 2024-08-03T22:15:58.117Z (7 months ago)
- Topics: api, classifier, image, label, learning, machine, sanic, simple, tensorflow
- Language: Python
- Homepage:
- Size: 105 KB
- Stars: 25
- Watchers: 3
- Forks: 8
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-sanic - Simple-image-classifier - A microservice for image classification, based on tensorflow (Resources / Built with Sanic)
README
# simple-image-classifier
Simple image classifier microservice using tensorflow

[](https://microbadger.com/images/gbnk0/simple-image-classifier)
[](https://microbadger.com/images/gbnk0/simple-image-classifier)
[](https://microbadger.com/images/gbnk0/simple-image-classifier)
[](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)
#### 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:
[](https://documenter.getpostman.com/view/3045659/RWMHM7ir)