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---
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
```python
# 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")
``` |