The project aims to generate datasets and train machine learning models to facilitate vision-based autonomous driving in vineyards.
A self-supervised learning approach is employed to train the model. The image below illustrates the general concept.
- Config: Contains configurations and paths for the project.
- Data: Includes the source vineyard video (not uploaded) and predictions.
- Model: Encompasses model training and inference, loss calculation, and optimizer settings.
- Tools: AutoSeg, a tool designed for automatic annotation.
- Utils: Houses useful functions for image manipulation.
- The dataset currently consists of a 10-minute video, walking through a vineyard.
- The dataset is automatically annotated using manual feature engineering.
- The annotations have a high degree of noise and uncertainty.
NOTE: This repository is not intended for reproduction and thus does not include the dataset or setup instructions.
Architecture: UNet: https://arxiv.org/abs/1505.04597