Table of Contents
The project carried out for the course "Machine and Deep Learning," consists of analyzing two types of datasets: images and text, using different machine learning algorithms.
- models_images: Folder with pre-trained models for images.
- models_text: Folder with pre-trained models for texts.
- notebook: Folder with notebooks used in the development of the project.
- accuracy_images_script.py: script to compute accuracy on custom images dataset with the pretrained models.
- accuracy_text_script.py: script to compute accuracy on custom texts dataset with the pretrained models
To get a local copy up and running follow these simple example steps.
Install the following libraries.
- requirements.txt
pip install -r requirements.txt
- Clone the repo
git clone https://github.com/GiuseTripodi/Image_Text_Analysis.git
Run one of the two scripts:
- accuracy_images_script.py: If you want to calculate accuracy on images with pretrained models.
- accuracy_text_script.py: If you want to calculate accuracy on text with pretrained models.
Information about script.
usage: accuracy_images_script.py [-h] [-s SCALE] [--version] images_path models_path
positional arguments:
images_path path of the directory with all the images
models_path path of the directory with all the images
optional arguments:
-h, --help show this help message and exit
-s SCALE, --scale SCALE
Percentage of the data to get, is a value between (0,1)
--version show program's version number and exit
To run the script:
python <script> [-s] <images_path> <models_path>
Example:
python accuracy_images_script.py --scale 0.05 "/home/Dataset/images" "/home/models_images"
Distributed under the MIT License. See LICENSE.txt
for more information.
Giuseppe Tripodi - @giuseppetripod3 - [email protected] - LinkedIn