tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: Segments-Sidewalk-SegFormer-B0
results: []
datasets:
- segments/sidewalk-semantic
pipeline_tag: image-segmentation
license: other
language:
- en
library_name: transformers
Model Details
- Model Name: Segments-Sidewalk-SegFormer-B0
- Model Type: Semantic Segmentation
- Base Model: nvidia/segformer-b0-finetuned-ade-512-512
- Fine-Tuning Dataset: Sidewalk-Semantic
Model Description
The Segments-Sidewalk-SegFormer-B0 model is a semantic segmentation model fine-tuned on the sidewalk-semantic dataset. It is based on the SegFormer (b0-sized) architecture and has been adapted for the task of segmenting sidewalk images into various classes, such as road surfaces, pedestrians, vehicles, and more.
Model Architecture
The model architecture is based on SegFormer, which utilizes a hierarchical Transformer Encoder and a lightweight all-MLP decoder head. This architecture has been proven effective in semantic segmentation tasks, and fine-tuning on the 'sidewalk-semantic' dataset allows it to learn to segment sidewalk images accurately.
Intended Uses
The Segments-Sidewalk-SegFormer-B0 model can be used for various applications in the context of sidewalk image analysis and understanding.
Some of the intended use cases include
- Semantic Segmentation: Use the model to perform pixel-level classification of sidewalk images, enabling the identification of different objects and features in the images, such as road surfaces, pedestrians, vehicles, and construction elements.
- Urban Planning: The model can assist in urban planning tasks by providing detailed information about sidewalk infrastructure, helping city planners make informed decisions.
- Autonomous Navigation: Deploy the model in autonomous vehicles or robots to enhance their understanding of the sidewalk environment, aiding in safe navigation.
Limitations
- Resolution Dependency: The model's performance may be sensitive to the resolution of the input images. Fine-tuning was performed at a specific resolution, so using significantly different resolutions may require additional adjustments.
- Hardware Requirements: Inference with deep learning models can be computationally intensive, requiring access to GPUs or other specialized hardware for real-time or efficient processing.
Ethical Considerations
When using and deploying the Segments-Sidewalk-SegFormer-B0 model, consider the following ethical considerations:
- Bias and Fairness: Carefully evaluate the dataset for biases that may be present and address them to avoid unfair or discriminatory outcomes in predictions, especially when dealing with human-related classes (e.g., pedestrians).
- Privacy: Be mindful of privacy concerns when processing sidewalk images, as they may contain personally identifiable information or capture private locations. Appropriate data anonymization and consent mechanisms should be in place.
- Transparency: Clearly communicate the model's capabilities and limitations to end-users and stakeholders, ensuring they understand the model's potential errors and uncertainties.
- Regulatory Compliance: Adhere to local and national regulations regarding the collection and processing of sidewalk images, especially if the data involves public spaces or private property.
- Accessibility: Ensure that the model's outputs and applications are accessible to individuals with disabilities and do not exclude any user group.
Usage
# Load model directly
from transformers import AutoFeatureExtractor, SegformerForSemanticSegmentation
extractor = AutoFeatureExtractor.from_pretrained("ayoubkirouane/Segments-Sidewalk-SegFormer-B0")
model = SegformerForSemanticSegmentation.from_pretrained("ayoubkirouane/Segments-Sidewalk-SegFormer-B0")