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README.md
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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.
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This model is an implementation of FastSam-X found [here](
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This repository provides scripts to run FastSam-X on Qualcomm® devices.
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More details on model performance across various devices, can be found
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[here](https://aihub.qualcomm.com/models/fastsam_x).
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- Number of parameters: 72.2M
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- Model size: 276 MB
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| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 45.786 ms | 5 - 21 MB | FP16 | NPU | [FastSam-X.so](https://huggingface.co/qualcomm/FastSam-X/blob/main/FastSam-X.so)
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## Installation
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```bash
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python -m qai_hub_models.models.fastsam_x.export
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```
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```
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```
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Get more details on FastSam-X's performance across various devices [here](https://aihub.qualcomm.com/models/fastsam_x).
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Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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## License
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## References
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* [Fast Segment Anything](https://arxiv.org/abs/2306.12156)
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* [Source Model Implementation](https://github.com/CASIA-IVA-Lab/FastSAM)
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## Community
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* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
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* For questions or feedback please [reach out to us](mailto:[email protected]).
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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.
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This model is an implementation of FastSam-X found [here]({source_repo}).
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This repository provides scripts to run FastSam-X on Qualcomm® devices.
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More details on model performance across various devices, can be found
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[here](https://aihub.qualcomm.com/models/fastsam_x).
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- Number of parameters: 72.2M
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- Model size: 276 MB
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| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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| FastSam-X | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 45.671 ms | 5 - 19 MB | FP16 | NPU | [FastSam-X.so](https://huggingface.co/qualcomm/FastSam-X/blob/main/FastSam-X.so) |
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| FastSam-X | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 48.826 ms | 0 - 157 MB | FP16 | NPU | [FastSam-X.onnx](https://huggingface.co/qualcomm/FastSam-X/blob/main/FastSam-X.onnx) |
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| FastSam-X | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 38.249 ms | 5 - 63 MB | FP16 | NPU | [FastSam-X.so](https://huggingface.co/qualcomm/FastSam-X/blob/main/FastSam-X.so) |
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| FastSam-X | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 39.188 ms | 1 - 157 MB | FP16 | NPU | [FastSam-X.onnx](https://huggingface.co/qualcomm/FastSam-X/blob/main/FastSam-X.onnx) |
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| FastSam-X | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 43.275 ms | 5 - 6 MB | FP16 | NPU | Use Export Script |
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| FastSam-X | SA8255 (Proxy) | SA8255P Proxy | QNN | 43.253 ms | 5 - 11 MB | FP16 | NPU | Use Export Script |
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| FastSam-X | SA8775 (Proxy) | SA8775P Proxy | QNN | 42.992 ms | 5 - 12 MB | FP16 | NPU | Use Export Script |
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| FastSam-X | SA8650 (Proxy) | SA8650P Proxy | QNN | 43.064 ms | 5 - 13 MB | FP16 | NPU | Use Export Script |
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| FastSam-X | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 90.623 ms | 5 - 67 MB | FP16 | NPU | Use Export Script |
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| FastSam-X | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 30.823 ms | 5 - 61 MB | FP16 | NPU | Use Export Script |
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| FastSam-X | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 31.678 ms | 1 - 77 MB | FP16 | NPU | [FastSam-X.onnx](https://huggingface.co/qualcomm/FastSam-X/blob/main/FastSam-X.onnx) |
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| FastSam-X | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 44.484 ms | 5 - 5 MB | FP16 | NPU | Use Export Script |
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| FastSam-X | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 49.5 ms | 139 - 139 MB | FP16 | NPU | [FastSam-X.onnx](https://huggingface.co/qualcomm/FastSam-X/blob/main/FastSam-X.onnx) |
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## Installation
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```bash
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python -m qai_hub_models.models.fastsam_x.export
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```
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```
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Profiling Results
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------------------------------------------------------------
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FastSam-X
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Device : Samsung Galaxy S23 (13)
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Runtime : QNN
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Estimated inference time (ms) : 45.7
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Estimated peak memory usage (MB): [5, 19]
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Total # Ops : 418
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Compute Unit(s) : NPU (418 ops)
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```
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Get more details on FastSam-X's performance across various devices [here](https://aihub.qualcomm.com/models/fastsam_x).
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Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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## License
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* The license for the original implementation of FastSam-X can be found [here](https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/LICENSE).
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* The license for the compiled assets for on-device deployment can be found [here](https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/LICENSE)
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## References
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* [Fast Segment Anything](https://arxiv.org/abs/2306.12156)
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* [Source Model Implementation](https://github.com/CASIA-IVA-Lab/FastSAM)
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## Community
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* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
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* For questions or feedback please [reach out to us](mailto:[email protected]).
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