FashionClassifier

FashionClassifier is a Python project aimed at classifying different types of clothing items into various categories such as t-shirts, blazers, dresses, and more. It utilizes machine learning algorithms to analyze and recognize patterns in images of clothing, allowing for accurate classification. This project provides a convenient solution for automating the categorization of clothing items, which can be useful in various applications such as e-commerce platforms, fashion analysis, and inventory management.

Prerequisites

Before running FashionClassifier, make sure you have the following requirements:

• Python 3.9 or higher
• OpenCV (cv2)

You can install OpenCV using pip:

   pip install opencv-python

If you're using Google Colab, you can install OpenCV using the following command in a code cell:

   !pip install opencv-python

Google Colab

Local Installation

To get started with FashionClassifier, follow these steps:

1. Open a shell and clone this repo

git clone https://github.com/jacquesbilombe/FashionClassifier

2. Create FashionClassifier environment.

2.1 Install make.

• For Windows:

  1- Open PowerShell (Windows + x)

  2- Install Chocolatey

Set-ExecutionPolicy Bypass -Scope Process -Force; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))

3- Verify Chocolatey install

choco

4- Make install

choco install make

• For Linux:

  1- Make install

sudo apt install make

2.2 Install Anaconda

If you don't have Anaconda, see how to install it here https://docs.anaconda.com/anaconda/install/linux/

3. Conda Torus environment creation.

Go to FashionClassifier/ folder and run the following code in Anaconda Prompt to create and install all pre-requirements

cd FashionClassifier
make setup

Finally, activate the conda environment

conda activate Torus

4. Run FashionClassifier

python main.py FLAG "train_folder" "teste_folder" "images_label_and_id.csv"

Terminal Example:

python main.py 0 "/compressed_images" "/original_images" "images.csv"

Obs:

• FLAG: 0 to use the trained ML model and 1 to train the ML model again.
• train_folder && teste_folder && images_label_and_id: if the line is empty "", the code will use the project dataset

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All

• Token for automation.