---
library_name: transformers
license: apache-2.0
datasets:
- segments/sidewalk-semantic
language:
- en
base_model:
- facebook/maskformer-swin-base-coco
pipeline_tag: image-segmentation
---

# 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

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- **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

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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

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### 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

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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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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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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