No project description provided
Project description
[![Build Status](https://travis-ci.org/accessai/access-niu.svg?branch=master)](https://travis-ci.org/accessai/access-niu)
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
# access-niu
This repository contains application to train models for Image classification and Regression tasks.
## Tasks
- [x] Create a basic app for training and inference
- [ ] Support for training regression models
- [ ] Support of training multi input/output models
- [ ] Incorporate Bayesian Inference for finding uncertainty in the predictions.
- [ ] Create Docker Image
- [ ] Support for serving the application with gunicorn
- [ ] Anything else?
## Installation
```bash
pip install access-niu
```
## Training
```bash
python -m access_niu.train --template access_niu/sample/colors/sample_template.yml
```
## Inference
```bash
python -m access_niu.wsgi --project ./colors
```
Now use this curl command to parse
```bash
curl -X POST \
http://localhost:8000/parse \
-F data=@image_leisure_0.jpg
```
## References
- This project is inspired from [RASA-NLU](https://github.com/RasaHQ/rasa) project.
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
# access-niu
This repository contains application to train models for Image classification and Regression tasks.
## Tasks
- [x] Create a basic app for training and inference
- [ ] Support for training regression models
- [ ] Support of training multi input/output models
- [ ] Incorporate Bayesian Inference for finding uncertainty in the predictions.
- [ ] Create Docker Image
- [ ] Support for serving the application with gunicorn
- [ ] Anything else?
## Installation
```bash
pip install access-niu
```
## Training
```bash
python -m access_niu.train --template access_niu/sample/colors/sample_template.yml
```
## Inference
```bash
python -m access_niu.wsgi --project ./colors
```
Now use this curl command to parse
```bash
curl -X POST \
http://localhost:8000/parse \
-F data=@image_leisure_0.jpg
```
## References
- This project is inspired from [RASA-NLU](https://github.com/RasaHQ/rasa) project.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
access-niu-0.0.1.tar.gz
(11.6 kB
view details)
File details
Details for the file access-niu-0.0.1.tar.gz
.
File metadata
- Download URL: access-niu-0.0.1.tar.gz
- Upload date:
- Size: 11.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/41.0.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | c56d783510979f83f269a79f07cbc991f2bf2abf6989c64b9a7a59ff592ae78b |
|
MD5 | beffcc018e3330079456b7a4d6bbaeeb |
|
BLAKE2b-256 | 43942fa8199fcedaab208d9659632a0d04c8ccc52dfa1b6c3e014c6b059d7d96 |