--- library_name: pytorch license: other tags: - android pipeline_tag: image-segmentation --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fastsam_x/web-assets/model_demo.png) # FastSam-X: 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-X found [here](https://github.com/CASIA-IVA-Lab/FastSAM). This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/fastsam_x) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary). Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device. ## Getting Started There are two ways to deploy this model on your device: ### Option 1: Download Pre-Exported Models Below are pre-exported model assets ready for deployment. | Runtime | Precision | Chipset | SDK Versions | Download | |---|---|---|---|---| | ONNX | float | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fastsam_x/releases/v0.46.0/fastsam_x-onnx-float.zip) | QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fastsam_x/releases/v0.46.0/fastsam_x-qnn_dlc-float.zip) | TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/fastsam_x/releases/v0.46.0/fastsam_x-tflite-float.zip) For more device-specific assets and performance metrics, visit **[FastSam-X on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/fastsam_x)**. ### Option 2: Export with Custom Configurations Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/fastsam_x) 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 This option is ideal if you need to customize the model beyond the default configuration provided here. See our repository for [FastSam-X on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/fastsam_x) for usage instructions. ## Model Details **Model Type:** Model_use_case.semantic_segmentation **Model Stats:** - Model checkpoint: fastsam-x.pt - Inference latency: RealTime - Input resolution: 640x640 - Number of parameters: 72.2M - Model size (float): 276 MB ## Performance Summary | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |---|---|---|---|---|---|--- | FastSam-X | ONNX | float | Snapdragon® X Elite | 46.486 ms | 139 - 139 MB | NPU | FastSam-X | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 36.497 ms | 4 - 267 MB | NPU | FastSam-X | ONNX | float | Qualcomm® QCS8550 (Proxy) | 46.077 ms | 11 - 14 MB | NPU | FastSam-X | ONNX | float | Qualcomm® QCS9075 | 73.748 ms | 11 - 19 MB | NPU | FastSam-X | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 27.37 ms | 12 - 187 MB | NPU | FastSam-X | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 18.362 ms | 1 - 183 MB | NPU | FastSam-X | QNN_DLC | float | Snapdragon® X Elite | 43.841 ms | 5 - 5 MB | NPU | FastSam-X | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 32.661 ms | 3 - 314 MB | NPU | FastSam-X | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 279.828 ms | 2 - 223 MB | NPU | FastSam-X | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 43.11 ms | 5 - 7 MB | NPU | FastSam-X | QNN_DLC | float | Qualcomm® SA8775P | 68.478 ms | 0 - 222 MB | NPU | FastSam-X | QNN_DLC | float | Qualcomm® QCS9075 | 70.434 ms | 7 - 17 MB | NPU | FastSam-X | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 93.211 ms | 2 - 392 MB | NPU | FastSam-X | QNN_DLC | float | Qualcomm® SA7255P | 279.828 ms | 2 - 223 MB | NPU | FastSam-X | QNN_DLC | float | Qualcomm® SA8295P | 77.966 ms | 0 - 296 MB | NPU | FastSam-X | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 25.29 ms | 0 - 222 MB | NPU | FastSam-X | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 17.889 ms | 5 - 242 MB | NPU | FastSam-X | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 32.534 ms | 3 - 443 MB | NPU | FastSam-X | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 279.179 ms | 4 - 269 MB | NPU | FastSam-X | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 42.096 ms | 4 - 43 MB | NPU | FastSam-X | TFLITE | float | Qualcomm® SA8775P | 68.042 ms | 4 - 269 MB | NPU | FastSam-X | TFLITE | float | Qualcomm® QCS9075 | 70.216 ms | 4 - 158 MB | NPU | FastSam-X | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 92.525 ms | 5 - 525 MB | NPU | FastSam-X | TFLITE | float | Qualcomm® SA7255P | 279.179 ms | 4 - 269 MB | NPU | FastSam-X | TFLITE | float | Qualcomm® SA8295P | 77.396 ms | 4 - 343 MB | NPU | FastSam-X | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 25.087 ms | 4 - 271 MB | NPU | FastSam-X | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 17.21 ms | 4 - 276 MB | NPU ## License * The license for the original implementation of FastSam-X can be found [here](https://github.com/CASIA-IVA-Lab/FastSAM/blob/main/LICENSE). ## References * [Fast Segment Anything](https://arxiv.org/abs/2306.12156) * [Source Model Implementation](https://github.com/CASIA-IVA-Lab/FastSAM) ## Community * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI. * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).