qualcomm/FastSam-S

FastSam-S: Optimized for Qualcomm Devices

The Fast Segment Anything Model (FastSAM) is a novel, real-time CNN-based solution for the Segment Anything task. This task is designed to segment any object within an image based on various possible user interaction prompts. The model performs competitively despite significantly reduced computation, making it a practical choice for a variety of vision tasks.

This is based on the implementation of FastSam-S found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.

Getting Started

Due to licensing restrictions, we cannot distribute pre-exported model assets for this model. Use the Qualcomm® AI Hub Models Python library to compile and export the model with your own:

• —Custom weights (e.g., fine-tuned checkpoints)
• —Custom input shapes
• —Target device and runtime configurations

See our repository for FastSam-S on GitHub for usage instructions.

Model Details

Model Type: Modelusecase.semantic_segmentation

Model Stats:

• —Inference latency: RealTime
• —Input resolution: 640x640
• —Model checkpoint: fastsam-s.pt
• —Model size (float): 45.1 MB
• —Number of parameters: 11.8M

Performance Summary

License

• —The license for the original implementation of FastSam-S can be found here.

References

• —Fast Segment Anything
• —Source Model Implementation

Community

• —Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
• —For questions or feedback please reach out to us.