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Project description
[](https://travis-ci.org/accessai/access-niu)
[](https://www.python.org/downloads/release/python-360/)
[](https://opensource.org/licenses/Apache-2.0)
[](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.
[](https://www.python.org/downloads/release/python-360/)
[](https://opensource.org/licenses/Apache-2.0)
[](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.
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