This repository contains the data and code used to generate the results described in the master thesis "An analysis of modeling and training decisions for greener computer vision systems".
In the data folder you will find two folders. In the raw folder, you will find a file containing the energetic data of
all the experiment runs and a file containing the model metrics (i.e., FLOPs, accuracy, precision, recall, and AUC). In
the processed folder, you will find the data obtained after integrating the files in the raw folder, and extracting
revelant features.
All the files inside the data folder have been saved using Panda's to_parquet function using the GZIP format.
The DL Training Profiling Dataset contains all the energy-related measurements taken during the training of the models tested in the study.
You will find a detailed description of the dataset in its dataset card.
The DL Training Energy Consumption Dataset contains all the energy-related measurements taken during the training of the models tested in the study together with their prediction quality.
You will find a detailed description of the dataset in its dataset card.
The repository contains a Jupyter Notebook with the code used to clean and analyze the data.
The software in this work is licensed under the Apache 2.0 License.
The files under the data folder are licensed under the CC-By Attribution 4.0 International.