szmazurek / Age_recognition_Cyfrovet

Age and disease prediction using machine learning.

Dog age assessment project led by CyfroVet, an initiative for veterinary medicine created by ACC Cyfronet AGH in Cracow.

The main goal of this work is to utilize machine learning models to improve the wellbeing of dogs. The system shows improvement beyond state-of-the-art, but the results are still not satisfying. CyfroVet is working on improving this along with approaching the problem with examination of videos showing the dogs. This repository contains the following files:

• train.py - pipeline and model that can be trained to classify dogs into three age groups.
• ga_dataset_assesment.py - a tool based on GA to exclude the least informative images from the dataset
• cnn_dog_breeds.py - a script with pipeline and model for dog breed classification task based on stanford dogs dataset
• tfrecords_writer.py - a script for saving image dataset in form of TFRecord files
• function_library.py - base of functions developed for some repeating tasks in this project. WARNING Some of them may not be compatibile with current scripts as they were developed in the early stages of the project

USAGE GUIDE

1. Install packages from requirements.txt (requirements file to be introduced soon)
2. train.py
   1. Arguments are described in the script. Provide necessary ones and start training.
   2. Configure wand.init call if using Weights and Biases to track your experiments (if no tracking is used wandb.init call and WandbCallback in fit method call can be removed).
   3. Run the script.
3. cnn_dog_breeds.py
   1. Open file in the editor.
   2. Create CNN model using create_cnn_network function or create CNN + ViT model using create_cnn_vit_network function.
   3. Dataset is automatically loaded from Tensorflow Datasets . If different data loading is needed please refer to the documentation under the link above.
   4. Configure wand.init call if using Weights and Biases to track your experiments (if no tracking is used wandb.init call and WandbCallback in fit method call can be removed).
   5. Run the script.
4. tfrecords_writer.py
   1. Arguments are described in the script. Provide necessary ones and start training.
5. ga_dataset_assesment (This algorithm is currently being tested on CIFAR dataset)
   1. Open file in the editor.
   2. Insert paths to:
      1. Images - into np.load function creating data_inputs_cifar variable
      2. Labels - into np.load function creating labels_cifar variable
      3. Invalid images - into np.load function creating data_inputs_noise variable
      4. Invalid labels - into np.load function creating labels_noise variable
      5. Initial data order (indexes of images after shuffling) - into solutions_dir variable
   3. If needed, configure the algorithm parameters specified with constants in the beginning of the file.
   4. Configure wand.init call if using Weights and Biases to track your experiments (if no tracking is used wandb.init call and WandbCallback in fit method call can be removed).
   5. Run the script.
6. function_library.py
   1. Import the file to your script and call the methods as needed.
   2. Be sure to use correct paths for DETECTOR and PREDICTOR - use files provided in utils folder.

DATASETS Currently published datasets are avaliable: https://tinyurl.com/dog-age-datasets For more information about this project please contact the owner via email: [e-mail]