sna89/segmentation_model

Model Card for Model ID

This model is a fine-tuned version of MaskFormer-Swin-Base-Coco, trained on the Sidewalk Dataset for instance segmentation. The original MaskFormer model is designed for dense prediction tasks like semantic segmentation and instance segmentation. It leverages the power of the Swin Transformer, a powerful vision model, to capture both local and global contextual information for improved segmentation performance.

Model Details

Model Description

• Developed by: Bowen Cheng, Alexander G. Schwing, Alexander Kirillov
• Model type: Segmentation
• Finetuned from model [optional]: MaskFormer-Swin-Base

Uses

This fine-tuned MaskFormer-Swin-Base-Coco model is optimized for instance segmentation in urban environments, specifically sidewalks. It can be used in applications such as smart city planning, autonomous vehicles, urban mobility, and surveillance systems, where accurate detection and segmentation of pedestrians, street furniture, and obstacles are essential for improving navigation, safety, and city infrastructure analysis.

Direct Use

can look for https://huggingface.co/facebook/maskformer-swin-base-coco for instructions

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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