roboticslaburjc-org/cross-dataset-visual-offroad-segmentation

Cross-Dataset Evaluation of Visual Semantic Segmentation Models for Off-Road Autonomous Driving

David Pascual-Hernández, Sergio Paniego, Roberto Calvo-Palomino, Inmaculada Mora-Jiménez, Jose María Cañas-Plaza

We provide weights for the models studied in Cross-Dataset Evaluation of Visual Semantic Segmentation Models for Off-Road Autonomous Driving. Directories nomenclature is defined as:

<dataset>-<ontology>-<data_augmentation>

• Dataset. Training dataset, either RELLIS-3D, GOOSE or a combination of both.
• Ontology. Ontology used during training. It can either be the original dataset ontology or our unified coarser ontology.
• Data augmentation. Data augmentation strategy, either light (used for inner-dataset experiments) or strong (used for cross-dataset experiments).

All models have been fine-tuned using the pre-trained weights provided by mmsegmentation for the Cityscapes dataset.