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https://github.com/bethropolis/myia
An Image classifier model and builder for binary image classification.
https://github.com/bethropolis/myia
ai-model image-classification model model-training-and-evaluation
Last synced: about 2 months ago
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An Image classifier model and builder for binary image classification.
- Host: GitHub
- URL: https://github.com/bethropolis/myia
- Owner: bethropolis
- License: mit
- Created: 2023-12-20T21:04:12.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-14T09:20:21.000Z (8 months ago)
- Last Synced: 2024-05-14T10:35:47.567Z (8 months ago)
- Topics: ai-model, image-classification, model, model-training-and-evaluation
- Language: Python
- Homepage: https://bethropolis.github.io/myia/
- Size: 69.2 MB
- Stars: 2
- Watchers: 2
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
MYIA
this is a nocode platform for training, testing, evaluating, and building image classifier models
Table of Contents
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Screenshots](#screenshots)
- [packages used](#packages-used)
- [Contributing](#contributing)
- [License](#license)
## Features
[](https://app.fossa.com/projects/git%2Bgithub.com%2Fbethropolis%2Fmyia?ref=badge_shield)
- model training
- model testing
- model evaluation
- file explorer
- bulk image upload
- screenshot script (go)
- image crawler/scrapper (go)
- graph generation
- dark/light mode
---
## Installation
1. Clone the repository.
```
git clone https://github.com/bethropolis/myia.git
```
2. Install the dependencies by running the following command:
```sh
pip install -r requirements.txt
```
3. Setup project:
```sh
python setup.py
```
4. create a virtual environment (optional):
```sh
python -m venv myenv
```
> activate the virtual environment
```bash
# windows
myenv\Scripts\activate
# linux / mac
source myenv/bin/activate
```
5. run the app:
```sh
python app.py
```
> open your browser and go to http://localhost:5000
---
## Usage
1. Upload images to the app
- upload images to the `training directory`
- head to `http://localhost:5000/directory?path=training/train` and upload your images
2. label the images
- Open the `train` page (`http://localhost:5000/train`)
- label the images either as good or bad by clicking the `thumbs up` or `thumbs down` button.
- the app can only generate binary classification models so you can only label the images as `good` or `bad`
- `thumbs up` for `good` which could represent classification `A`
- `thumbs down` for `bad` which could represent classification `B`
> currently the app only supports two labels `good` and `bad`
3. build the model
- To build the model, head to the `home` page (`http://localhost:5000/`) and click the `build model` button
- In the next page you will have to input:
`No of epochs` - the number of times the model will be trained (default is 15)
`No of layers` - the number of layers the model will have (default is 3)
`Model name` - the name of the model (default is `myia_image_classifier`)
- click the `build model` button to start building the model
The model will be saved in the `model/image_model` directory as a `keras` model
> Note: The higher the number of epochs the longer it will take to build the model
4. test the model
- To test the model, open the `test` page (`http://localhost:5000/test`) and upload an image to test the model with
or test with images in the `test` directory (`http://localhost:5000/directory?path=training/test`)
5. evaluate the model
- To evaluate the model, open the `evaluate` page (`http://localhost:5000/evaluate`) and upload an image to evaluate the model with
or evaluate with images in the `evaluate` directory (`http://localhost:5000/directory?path=model/evaluation`)
- The evaluation results will be saved in the `model` directory as a `json` file and a graph will be generated and saved in the `static` directory as a `png` file
---
## Screenshots
| Image | Description |
| ----------------------------------------------------------------------------------------------------- | --------------------------------------------- |
| 
| A screenshot of the Home page |
| 
| A screenshot of the Training page |
| 
| A screenshot of the Testing page |
| 
| A screenshot of the Evaluation page |
| 
| A screenshot of a directory |
| 
| A screenshot of a directory |
| 
| A screenshot of the page for building a model |
---
## packages used
- [Flask](https://flask.palletsprojects.com/)
- [Pillow](https://pillow.readthedocs.io/en/stable/)
- [Tensorflow](https://www.tensorflow.org/)
- [Keras](https://keras.io/)
- [Numpy](https://numpy.org/doc/stable/user/whatisnumpy.html)
- [matplotlib](https://matplotlib.org/)
- [chromedp](https://github.com/chromedp/chromedp)
- [goquery](https://github.com/PuerkitoBio/goquery)
---
## Contributing
Feel free to ping me a pull requests if you want to contribute.
## License
This project is licensed under the [MIT License](LICENSE).
happy coding 💜
[](https://app.fossa.com/projects/git%2Bgithub.com%2Fbethropolis%2Fmyia?ref=badge_large)