no code implementations • 21 Mar 2023 • Abraham Sánchez, Raúl Nanclares, Alexander Quevedo, Ulises Pelagio, Alejandra Aguilar, Gabriela Calvario, E. Ulises Moya-Sánchez
To achieve this, we solve real-world deep learning problems in the very specific context of agave crop segmentation such as lack of data, low quality labels, highly imbalanced data, and low model performance.
no code implementations • 26 Jan 2022 • Alexander Quevedo, Abraham Sánchez, Raul Nancláres, Diana P. Montoya, Juan Pacho, Jorge Martínez, E. Ulises Moya-Sánchez
In this work, we present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis to handle these problems for the Jalisco region.
1 code implementation • 14 Sep 2021 • E. Ulises Moya-Sánchez, Sebastiá Xambo-Descamps, Abraham Sánchez, Sebastián Salazar-Colores, Ulises Cortés
This new trainable layer is capable of coping with image classification even with large contrast variations.