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--- |
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tags: |
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- vision |
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- image-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: Segments-Sidewalk-SegFormer-B0 |
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results: [] |
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datasets: |
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- segments/sidewalk-semantic |
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pipeline_tag: image-segmentation |
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license: other |
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language: |
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- en |
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library_name: transformers |
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--- |
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## Model Details |
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+ **Model Name**: Segments-Sidewalk-SegFormer-B0 |
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+ **Model Type**: Semantic Segmentation |
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+ **Base Model**: nvidia/segformer-b0-finetuned-ade-512-512 |
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+ **Fine-Tuning Dataset**: Sidewalk-Semantic |
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## Model Description |
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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. |
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## Model Architecture |
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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. |
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## Intended Uses |
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The **Segments-Sidewalk-SegFormer-B0** model can be used for various applications in the context of sidewalk image analysis and understanding. |
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**Some of the intended use cases include** |
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+ **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. |
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+ **Urban Planning**: The model can assist in urban planning tasks by providing detailed information about sidewalk infrastructure, helping city planners make informed decisions. |
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+ **Autonomous Navigation**: Deploy the model in autonomous vehicles or robots to enhance their understanding of the sidewalk environment, aiding in safe navigation. |
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## Limitations |
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+ **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. |
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+ **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. |
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## Ethical Considerations |
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When using and deploying the **Segments-Sidewalk-SegFormer-B0** model, consider the following ethical considerations: |
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+ **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). |
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+ **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. |
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+ **Transparency**: Clearly communicate the model's capabilities and limitations to end-users and stakeholders, ensuring they understand the model's potential errors and uncertainties. |
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+ **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. |
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+ **Accessibility**: Ensure that the model's outputs and applications are accessible to individuals with disabilities and do not exclude any user group. |
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## Usage |
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```python |
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# Load model directly |
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from transformers import AutoFeatureExtractor, SegformerForSemanticSegmentation |
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extractor = AutoFeatureExtractor.from_pretrained("ayoubkirouane/Segments-Sidewalk-SegFormer-B0") |
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model = SegformerForSemanticSegmentation.from_pretrained("ayoubkirouane/Segments-Sidewalk-SegFormer-B0") |
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``` |
