clean-img-classification-dataset

• Medium Article

Setting up the project environment

Run in root of repository

1. Install virtualenv if you haven't already:

   • Note: Depending on your system and how Python is installed, you may need to use pip instead of pip3 to ensure you are using Python 3.

   pip3 install virtualenv
   

2. Create a virtual environment in the project directory:

   virtualenv clean-img-classification-dataset-env
   

3. Activate the virtual environment:

   • On Windows:

     .\clean-img-classification-dataset-env\Scripts\activate
     

   • On Unix or MacOS:

     source clean-img-classification-dataset-env/bin/activate
     

4. Install the required dependencies:

   pip3 install -r requirements.txt
   

Run the script

1. First ensure you have saved your embeddings, labels, and paths to a .npz file. You can insert the following code into your script where the embeddings, labels, and paths are generated.

   import numpy as np
   
   embeddings = [] # list of embeddings
   labels = [] # list of labels
   image_paths = [] # list of img paths
   
   np.savez_compressed(
       "image_embeddings_labels_paths.npz",
       embeddings=np.vstack(embeddings),  # Stack embeddings into a 2D array
       labels=np.array(labels),  # Convert labels to a 1D numpy array
       image_paths=np.array(image_paths)  # Convert image paths to a 1D numpy array
   )
   

2. Update the NPZ_PATH variable in clean_dataset.py to the path of your .npz file.
3. Run the script:

   python3 clean_dataset.py