predict-photographer-editing-style

overview

Goal: create a model that can predict the identity of the photographer by analysing the editing style of their edited photographs
Data:
- ~7000 images (we can use much less for testing purposes) from Cats and Dogs Breeds Classification Oxford Dataset (downloaded from here)
- artificial labels created by splitting image (by their global features) into 30 different clusters that are corresponded to photographers (see below function create_image_features_df() )

What this repository do? 🚀

Setup

install python 3.8
Download and install with pip (or use locally) this package
install CUDA
setup conda env and install requirements by running pip install -r requirements.txt in your terminal
download the dataset from [Cats and Dogs Breeds Classification Oxford Dataset and save it to the "./data" repository
run function create_image_features_df() in file images_autoencoder.py to create a dataframe with the features of the images divided artificial in 30 different photographers by clustering the images global features space with KMeans

System design

Model 1: AutoEncoder

creates an autoencoder model to reduce the dimensionality of the images by using both pixels values and global features as loss between input and output images
run main in images_autoencoder.py
- create and train an AutoEncoder
- save encoder for later use
- monitor training process with TensorBoard
the result is an encoder that can encode new images in a latent we created

Model 2: global feature predictor by photographer

run main in user_predictor.py
- create a random initial tensor with 3 (photographers embedding space dimensionality) over N (number of unique photographers)
- create a mixed model that uses the output of the embedding layer of the AutoEncoder as input concatenated with the random photographers embedding tensor
- enable gradients on both the model weights and the photographer embeddings (the last layer in the network)
- trains the model to predict the editing style of the photographer based on the image
- we have created two things here:
  - a model that can predict the editing style of a photographer based on their images
  - a photographers embedding space that can be used to various tasks (such as find similar photographers...)

images embedding space example in 2D projection (using PCA)

as the steps progress the embedding space is getting more and more stable

the embedding space is divided into 30 clusters (photographers) using k-means clustering algorithm, this is the artificial ground truth for the model we are generating