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Segment-Anything-Model

Image Segmentation
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Model card Files Community

Segment-Anything-Model: Optimized for Mobile Deployment

High-quality segmentation mask generation around any object in an image with simple input prompt

Transformer based encoder-decoder where prompts specify what to segment in an image thereby allowing segmentation without the need for additional training. The image encoder generates embeddings and the lightweight decoder operates on the embeddings for point and mask based image segmentation.

This model is an implementation of Segment-Anything-Model found here.

This repository provides scripts to run Segment-Anything-Model on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Semantic segmentation
  • Model Stats:
    • Model checkpoint: vit_l
    • Input resolution: 720p (720x1280)
    • Number of parameters (SAMDecoder): 5.11M
    • Model size (SAMDecoder): 19.6 MB
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
SAMDecoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 7.455 ms 0 - 32 MB FP16 NPU Segment-Anything-Model.tflite
SAMDecoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 6.857 ms 6 - 9 MB FP16 NPU Segment-Anything-Model.so
SAMDecoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 11.054 ms 1 - 66 MB FP16 NPU Segment-Anything-Model.onnx
SAMDecoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 5.215 ms 0 - 37 MB FP16 NPU Segment-Anything-Model.tflite
SAMDecoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 4.318 ms 4 - 23 MB FP16 NPU Segment-Anything-Model.so
SAMDecoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 7.82 ms 2 - 57 MB FP16 NPU Segment-Anything-Model.onnx
SAMDecoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 5.108 ms 0 - 38 MB FP16 NPU Segment-Anything-Model.tflite
SAMDecoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 4.824 ms 4 - 46 MB FP16 NPU Use Export Script
SAMDecoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 8.303 ms 2 - 50 MB FP16 NPU Segment-Anything-Model.onnx
SAMDecoder SA7255P ADP SA7255P TFLITE 53.0 ms 0 - 31 MB FP16 NPU Segment-Anything-Model.tflite
SAMDecoder SA7255P ADP SA7255P QNN 49.808 ms 4 - 11 MB FP16 NPU Use Export Script
SAMDecoder SA8255 (Proxy) SA8255P Proxy TFLITE 7.476 ms 0 - 28 MB FP16 NPU Segment-Anything-Model.tflite
SAMDecoder SA8255 (Proxy) SA8255P Proxy QNN 6.857 ms 4 - 6 MB FP16 NPU Use Export Script
SAMDecoder SA8295P ADP SA8295P TFLITE 10.548 ms 0 - 30 MB FP16 NPU Segment-Anything-Model.tflite
SAMDecoder SA8295P ADP SA8295P QNN 9.556 ms 0 - 10 MB FP16 NPU Use Export Script
SAMDecoder SA8650 (Proxy) SA8650P Proxy TFLITE 7.459 ms 0 - 35 MB FP16 NPU Segment-Anything-Model.tflite
SAMDecoder SA8650 (Proxy) SA8650P Proxy QNN 6.875 ms 4 - 7 MB FP16 NPU Use Export Script
SAMDecoder SA8775P ADP SA8775P TFLITE 10.483 ms 0 - 32 MB FP16 NPU Segment-Anything-Model.tflite
SAMDecoder SA8775P ADP SA8775P QNN 9.676 ms 0 - 8 MB FP16 NPU Use Export Script
SAMDecoder QCS8275 (Proxy) QCS8275 Proxy TFLITE 53.0 ms 0 - 31 MB FP16 NPU Segment-Anything-Model.tflite
SAMDecoder QCS8275 (Proxy) QCS8275 Proxy QNN 49.808 ms 4 - 11 MB FP16 NPU Use Export Script
SAMDecoder QCS8550 (Proxy) QCS8550 Proxy TFLITE 7.463 ms 0 - 29 MB FP16 NPU Segment-Anything-Model.tflite
SAMDecoder QCS8550 (Proxy) QCS8550 Proxy QNN 6.851 ms 4 - 7 MB FP16 NPU Use Export Script
SAMDecoder QCS9075 (Proxy) QCS9075 Proxy TFLITE 10.483 ms 0 - 32 MB FP16 NPU Segment-Anything-Model.tflite
SAMDecoder QCS9075 (Proxy) QCS9075 Proxy QNN 9.676 ms 0 - 8 MB FP16 NPU Use Export Script
SAMDecoder QCS8450 (Proxy) QCS8450 Proxy TFLITE 9.166 ms 0 - 38 MB FP16 NPU Segment-Anything-Model.tflite
SAMDecoder QCS8450 (Proxy) QCS8450 Proxy QNN 8.229 ms 4 - 44 MB FP16 NPU Use Export Script
SAMDecoder Snapdragon X Elite CRD Snapdragon® X Elite QNN 7.424 ms 4 - 4 MB FP16 NPU Use Export Script
SAMDecoder Snapdragon X Elite CRD Snapdragon® X Elite ONNX 14.715 ms 13 - 13 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart1 Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 208.174 ms 12 - 78 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart1 Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 175.417 ms 14 - 16 MB FP16 NPU Segment-Anything-Model.so
SAMEncoderPart1 Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 165.892 ms 24 - 127 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart1 Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 147.8 ms 11 - 658 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart1 Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 126.997 ms 12 - 28 MB FP16 NPU Segment-Anything-Model.so
SAMEncoderPart1 Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 122.647 ms 24 - 694 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart1 Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 144.144 ms 10 - 664 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart1 Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 141.645 ms 12 - 659 MB FP16 NPU Use Export Script
SAMEncoderPart1 Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 108.212 ms 23 - 668 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart1 SA7255P ADP SA7255P TFLITE 1173.603 ms 0 - 644 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart1 SA7255P ADP SA7255P QNN 1103.265 ms 4 - 12 MB FP16 NPU Use Export Script
SAMEncoderPart1 SA8255 (Proxy) SA8255P Proxy TFLITE 209.255 ms 12 - 69 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart1 SA8255 (Proxy) SA8255P Proxy QNN 179.225 ms 12 - 14 MB FP16 NPU Use Export Script
SAMEncoderPart1 SA8295P ADP SA8295P TFLITE 242.469 ms 12 - 640 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart1 SA8295P ADP SA8295P QNN 207.255 ms 0 - 11 MB FP16 NPU Use Export Script
SAMEncoderPart1 SA8650 (Proxy) SA8650P Proxy TFLITE 208.157 ms 12 - 69 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart1 SA8650 (Proxy) SA8650P Proxy QNN 177.435 ms 13 - 16 MB FP16 NPU Use Export Script
SAMEncoderPart1 SA8775P ADP SA8775P TFLITE 249.667 ms 12 - 656 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart1 SA8775P ADP SA8775P QNN 211.757 ms 1 - 9 MB FP16 NPU Use Export Script
SAMEncoderPart1 QCS8275 (Proxy) QCS8275 Proxy TFLITE 1173.603 ms 0 - 644 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart1 QCS8275 (Proxy) QCS8275 Proxy QNN 1103.265 ms 4 - 12 MB FP16 NPU Use Export Script
SAMEncoderPart1 QCS8550 (Proxy) QCS8550 Proxy TFLITE 207.823 ms 12 - 76 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart1 QCS8550 (Proxy) QCS8550 Proxy QNN 177.311 ms 12 - 15 MB FP16 NPU Use Export Script
SAMEncoderPart1 QCS9075 (Proxy) QCS9075 Proxy TFLITE 249.667 ms 12 - 656 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart1 QCS9075 (Proxy) QCS9075 Proxy QNN 211.757 ms 1 - 9 MB FP16 NPU Use Export Script
SAMEncoderPart1 QCS8450 (Proxy) QCS8450 Proxy TFLITE 232.379 ms 12 - 993 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart1 QCS8450 (Proxy) QCS8450 Proxy QNN 223.767 ms 12 - 971 MB FP16 NPU Use Export Script
SAMEncoderPart1 Snapdragon X Elite CRD Snapdragon® X Elite QNN 174.73 ms 12 - 12 MB FP16 NPU Use Export Script
SAMEncoderPart1 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 180.556 ms 38 - 38 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart2 Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 663.719 ms 12 - 112 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart2 Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 733.441 ms 13 - 15 MB FP16 NPU Segment-Anything-Model.so
SAMEncoderPart2 Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 749.685 ms 18 - 220 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart2 Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 545.405 ms 12 - 28 MB FP16 NPU Segment-Anything-Model.so
SAMEncoderPart2 Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 591.244 ms 24 - 1422 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart2 Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 473.547 ms 11 - 1142 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart2 Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 527.677 ms 12 - 1112 MB FP16 NPU Use Export Script
SAMEncoderPart2 Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 460.831 ms 36 - 1406 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart2 SA7255P ADP SA7255P QNN 1885.474 ms 4 - 11 MB FP16 NPU Use Export Script
SAMEncoderPart2 SA8255 (Proxy) SA8255P Proxy TFLITE 656.701 ms 11 - 93 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart2 SA8255 (Proxy) SA8255P Proxy QNN 731.619 ms 12 - 15 MB FP16 NPU Use Export Script
SAMEncoderPart2 SA8295P ADP SA8295P TFLITE 707.412 ms 12 - 1172 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart2 SA8295P ADP SA8295P QNN 786.133 ms 0 - 11 MB FP16 NPU Use Export Script
SAMEncoderPart2 SA8650 (Proxy) SA8650P Proxy TFLITE 684.922 ms 12 - 104 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart2 SA8650 (Proxy) SA8650P Proxy QNN 737.991 ms 12 - 14 MB FP16 NPU Use Export Script
SAMEncoderPart2 SA8775P ADP SA8775P TFLITE 702.795 ms 0 - 1144 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart2 SA8775P ADP SA8775P QNN 740.054 ms 2 - 8 MB FP16 NPU Use Export Script
SAMEncoderPart2 QCS8275 (Proxy) QCS8275 Proxy QNN 1885.474 ms 4 - 11 MB FP16 NPU Use Export Script
SAMEncoderPart2 QCS8550 (Proxy) QCS8550 Proxy TFLITE 653.785 ms 12 - 103 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart2 QCS8550 (Proxy) QCS8550 Proxy QNN 734.787 ms 12 - 14 MB FP16 NPU Use Export Script
SAMEncoderPart2 QCS9075 (Proxy) QCS9075 Proxy TFLITE 702.795 ms 0 - 1144 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart2 QCS9075 (Proxy) QCS9075 Proxy QNN 740.054 ms 2 - 8 MB FP16 NPU Use Export Script
SAMEncoderPart2 Snapdragon X Elite CRD Snapdragon® X Elite QNN 633.697 ms 12 - 12 MB FP16 NPU Use Export Script
SAMEncoderPart2 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 745.618 ms 53 - 53 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart3 Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 669.851 ms 12 - 110 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart3 Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 741.286 ms 12 - 15 MB FP16 NPU Segment-Anything-Model.so
SAMEncoderPart3 Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 748.764 ms 0 - 183 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart3 Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 545.914 ms 12 - 27 MB FP16 NPU Segment-Anything-Model.so
SAMEncoderPart3 Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 580.246 ms 36 - 1433 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart3 Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 428.668 ms 11 - 1141 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart3 Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 576.105 ms 12 - 1112 MB FP16 NPU Use Export Script
SAMEncoderPart3 Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 459.599 ms 35 - 1408 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart3 SA7255P ADP SA7255P QNN 1886.342 ms 4 - 10 MB FP16 NPU Use Export Script
SAMEncoderPart3 SA8255 (Proxy) SA8255P Proxy TFLITE 664.029 ms 12 - 111 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart3 SA8255 (Proxy) SA8255P Proxy QNN 741.521 ms 12 - 15 MB FP16 NPU Use Export Script
SAMEncoderPart3 SA8295P ADP SA8295P TFLITE 707.193 ms 12 - 1175 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart3 SA8295P ADP SA8295P QNN 784.205 ms 0 - 11 MB FP16 NPU Use Export Script
SAMEncoderPart3 SA8650 (Proxy) SA8650P Proxy TFLITE 672.362 ms 12 - 105 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart3 SA8650 (Proxy) SA8650P Proxy QNN 737.027 ms 12 - 15 MB FP16 NPU Use Export Script
SAMEncoderPart3 SA8775P ADP SA8775P TFLITE 703.475 ms 0 - 1144 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart3 SA8775P ADP SA8775P QNN 738.516 ms 1 - 9 MB FP16 NPU Use Export Script
SAMEncoderPart3 QCS8275 (Proxy) QCS8275 Proxy QNN 1886.342 ms 4 - 10 MB FP16 NPU Use Export Script
SAMEncoderPart3 QCS8550 (Proxy) QCS8550 Proxy TFLITE 666.947 ms 12 - 103 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart3 QCS8550 (Proxy) QCS8550 Proxy QNN 738.236 ms 12 - 15 MB FP16 NPU Use Export Script
SAMEncoderPart3 QCS9075 (Proxy) QCS9075 Proxy TFLITE 703.475 ms 0 - 1144 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart3 QCS9075 (Proxy) QCS9075 Proxy QNN 738.516 ms 1 - 9 MB FP16 NPU Use Export Script
SAMEncoderPart3 Snapdragon X Elite CRD Snapdragon® X Elite QNN 644.658 ms 12 - 12 MB FP16 NPU Use Export Script
SAMEncoderPart3 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 751.206 ms 51 - 51 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart4 Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 674.618 ms 12 - 104 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart4 Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 746.674 ms 12 - 14 MB FP16 NPU Segment-Anything-Model.so
SAMEncoderPart4 Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 736.999 ms 24 - 228 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart4 Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 545.535 ms 12 - 28 MB FP16 NPU Segment-Anything-Model.so
SAMEncoderPart4 Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 585.741 ms 35 - 1436 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart4 Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 472.497 ms 11 - 1140 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart4 Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 530.712 ms 12 - 1113 MB FP16 NPU Use Export Script
SAMEncoderPart4 Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 439.426 ms 36 - 1409 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart4 SA7255P ADP SA7255P QNN 1884.573 ms 0 - 9 MB FP16 NPU Use Export Script
SAMEncoderPart4 SA8255 (Proxy) SA8255P Proxy TFLITE 653.028 ms 12 - 103 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart4 SA8255 (Proxy) SA8255P Proxy QNN 744.778 ms 12 - 15 MB FP16 NPU Use Export Script
SAMEncoderPart4 SA8295P ADP SA8295P TFLITE 705.387 ms 12 - 1174 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart4 SA8295P ADP SA8295P QNN 782.232 ms 0 - 11 MB FP16 NPU Use Export Script
SAMEncoderPart4 SA8650 (Proxy) SA8650P Proxy TFLITE 656.723 ms 12 - 112 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart4 SA8650 (Proxy) SA8650P Proxy QNN 749.36 ms 14 - 16 MB FP16 NPU Use Export Script
SAMEncoderPart4 SA8775P ADP SA8775P TFLITE 711.855 ms 0 - 1145 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart4 SA8775P ADP SA8775P QNN 740.717 ms 1 - 9 MB FP16 NPU Use Export Script
SAMEncoderPart4 QCS8275 (Proxy) QCS8275 Proxy QNN 1884.573 ms 0 - 9 MB FP16 NPU Use Export Script
SAMEncoderPart4 QCS8550 (Proxy) QCS8550 Proxy TFLITE 660.012 ms 12 - 104 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart4 QCS8550 (Proxy) QCS8550 Proxy QNN 746.363 ms 12 - 14 MB FP16 NPU Use Export Script
SAMEncoderPart4 QCS9075 (Proxy) QCS9075 Proxy TFLITE 711.855 ms 0 - 1145 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart4 QCS9075 (Proxy) QCS9075 Proxy QNN 740.717 ms 1 - 9 MB FP16 NPU Use Export Script
SAMEncoderPart4 Snapdragon X Elite CRD Snapdragon® X Elite QNN 623.063 ms 12 - 12 MB FP16 NPU Use Export Script
SAMEncoderPart4 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 734.372 ms 51 - 51 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart5 Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 663.865 ms 12 - 104 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart5 Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 745.203 ms 12 - 15 MB FP16 NPU Segment-Anything-Model.so
SAMEncoderPart5 Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 738.266 ms 24 - 216 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart5 Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 544.125 ms 12 - 27 MB FP16 NPU Segment-Anything-Model.so
SAMEncoderPart5 Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 588.519 ms 23 - 1423 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart5 Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 427.751 ms 12 - 1142 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart5 Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 580.371 ms 12 - 1111 MB FP16 NPU Use Export Script
SAMEncoderPart5 Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 462.137 ms 24 - 1397 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart5 SA8255 (Proxy) SA8255P Proxy TFLITE 674.516 ms 12 - 109 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart5 SA8255 (Proxy) SA8255P Proxy QNN 727.73 ms 12 - 15 MB FP16 NPU Use Export Script
SAMEncoderPart5 SA8295P ADP SA8295P TFLITE 706.687 ms 12 - 1172 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart5 SA8295P ADP SA8295P QNN 784.357 ms 0 - 10 MB FP16 NPU Use Export Script
SAMEncoderPart5 SA8650 (Proxy) SA8650P Proxy TFLITE 655.632 ms 12 - 103 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart5 SA8650 (Proxy) SA8650P Proxy QNN 735.971 ms 16 - 18 MB FP16 NPU Use Export Script
SAMEncoderPart5 SA8775P ADP SA8775P TFLITE 703.846 ms 0 - 1145 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart5 SA8775P ADP SA8775P QNN 740.709 ms 1 - 8 MB FP16 NPU Use Export Script
SAMEncoderPart5 QCS8550 (Proxy) QCS8550 Proxy TFLITE 671.243 ms 12 - 104 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart5 QCS8550 (Proxy) QCS8550 Proxy QNN 742.409 ms 12 - 14 MB FP16 NPU Use Export Script
SAMEncoderPart5 QCS9075 (Proxy) QCS9075 Proxy TFLITE 703.846 ms 0 - 1145 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart5 QCS9075 (Proxy) QCS9075 Proxy QNN 740.709 ms 1 - 8 MB FP16 NPU Use Export Script
SAMEncoderPart5 Snapdragon X Elite CRD Snapdragon® X Elite QNN 624.498 ms 12 - 12 MB FP16 NPU Use Export Script
SAMEncoderPart5 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 731.48 ms 53 - 53 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart6 Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 676.887 ms 12 - 104 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart6 Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 745.987 ms 12 - 15 MB FP16 NPU Segment-Anything-Model.so
SAMEncoderPart6 Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 742.01 ms 12 - 209 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart6 Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 544.172 ms 12 - 28 MB FP16 NPU Segment-Anything-Model.so
SAMEncoderPart6 Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 589.508 ms 32 - 1433 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart6 Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 430.345 ms 11 - 1139 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart6 Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 569.23 ms 12 - 1111 MB FP16 NPU Use Export Script
SAMEncoderPart6 Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 460.98 ms 36 - 1407 MB FP16 NPU Segment-Anything-Model.onnx
SAMEncoderPart6 SA8255 (Proxy) SA8255P Proxy TFLITE 669.101 ms 12 - 113 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart6 SA8255 (Proxy) SA8255P Proxy QNN 746.045 ms 12 - 15 MB FP16 NPU Use Export Script
SAMEncoderPart6 SA8295P ADP SA8295P TFLITE 708.764 ms 12 - 1174 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart6 SA8295P ADP SA8295P QNN 783.365 ms 0 - 11 MB FP16 NPU Use Export Script
SAMEncoderPart6 SA8650 (Proxy) SA8650P Proxy TFLITE 633.429 ms 12 - 104 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart6 SA8650 (Proxy) SA8650P Proxy QNN 749.732 ms 12 - 14 MB FP16 NPU Use Export Script
SAMEncoderPart6 SA8775P ADP SA8775P TFLITE 711.885 ms 0 - 1144 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart6 QCS8550 (Proxy) QCS8550 Proxy TFLITE 674.605 ms 12 - 114 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart6 QCS8550 (Proxy) QCS8550 Proxy QNN 741.375 ms 12 - 14 MB FP16 NPU Use Export Script
SAMEncoderPart6 QCS9075 (Proxy) QCS9075 Proxy TFLITE 711.885 ms 0 - 1144 MB FP16 NPU Segment-Anything-Model.tflite
SAMEncoderPart6 Snapdragon X Elite CRD Snapdragon® X Elite QNN 641.457 ms 12 - 12 MB FP16 NPU Use Export Script
SAMEncoderPart6 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 736.709 ms 51 - 51 MB FP16 NPU Segment-Anything-Model.onnx

Installation

Install the package via pip:

pip install "qai-hub-models[sam]"

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub with your Qualcomm® ID. Once signed in navigate to Account -> Settings -> API Token.

With this API token, you can configure your client to run models on the cloud hosted devices.

qai-hub configure --api_token API_TOKEN

Navigate to docs for more information.

Demo off target

The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input.

python -m qai_hub_models.models.sam.demo

The above demo runs a reference implementation of pre-processing, model inference, and post processing.

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.sam.demo

Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following:

  • Performance check on-device on a cloud-hosted device
  • Downloads compiled assets that can be deployed on-device for Android.
  • Accuracy check between PyTorch and on-device outputs.
python -m qai_hub_models.models.sam.export

Profiling Results
------------------------------------------------------------
SAMDecoder
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 7.5                    
Estimated peak memory usage (MB): [0, 32]                
Total # Ops                     : 845                    
Compute Unit(s)                 : NPU (845 ops)          

------------------------------------------------------------
SAMEncoderPart1
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 208.2                  
Estimated peak memory usage (MB): [12, 78]               
Total # Ops                     : 584                    
Compute Unit(s)                 : NPU (584 ops)          

------------------------------------------------------------
SAMEncoderPart2
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 663.7                  
Estimated peak memory usage (MB): [12, 112]              
Total # Ops                     : 572                    
Compute Unit(s)                 : NPU (572 ops)          

------------------------------------------------------------
SAMEncoderPart3
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 669.9                  
Estimated peak memory usage (MB): [12, 110]              
Total # Ops                     : 572                    
Compute Unit(s)                 : NPU (572 ops)          

------------------------------------------------------------
SAMEncoderPart4
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 674.6                  
Estimated peak memory usage (MB): [12, 104]              
Total # Ops                     : 572                    
Compute Unit(s)                 : NPU (572 ops)          

------------------------------------------------------------
SAMEncoderPart5
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 663.9                  
Estimated peak memory usage (MB): [12, 104]              
Total # Ops                     : 572                    
Compute Unit(s)                 : NPU (572 ops)          

------------------------------------------------------------
SAMEncoderPart6
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 676.9                  
Estimated peak memory usage (MB): [12, 104]              
Total # Ops                     : 573                    
Compute Unit(s)                 : NPU (573 ops)

How does this work?

This export script leverages Qualcomm® AI Hub to optimize, validate, and deploy this model on-device. Lets go through each step below in detail:

Step 1: Compile model for on-device deployment

To compile a PyTorch model for on-device deployment, we first trace the model in memory using the jit.trace and then call the submit_compile_job API.

import torch

import qai_hub as hub
from qai_hub_models.models.sam import Model

# Load the model
model = Model.from_pretrained()
decoder_model = model.decoder
encoder_splits[0]_model = model.encoder_splits[0]
encoder_splits[1]_model = model.encoder_splits[1]
encoder_splits[2]_model = model.encoder_splits[2]
encoder_splits[3]_model = model.encoder_splits[3]
encoder_splits[4]_model = model.encoder_splits[4]
encoder_splits[5]_model = model.encoder_splits[5]

# Device
device = hub.Device("Samsung Galaxy S23")

# Trace model
decoder_input_shape = decoder_model.get_input_spec()
decoder_sample_inputs = decoder_model.sample_inputs()

traced_decoder_model = torch.jit.trace(decoder_model, [torch.tensor(data[0]) for _, data in decoder_sample_inputs.items()])

# Compile model on a specific device
decoder_compile_job = hub.submit_compile_job(
    model=traced_decoder_model ,
    device=device,
    input_specs=decoder_model.get_input_spec(),
)

# Get target model to run on-device
decoder_target_model = decoder_compile_job.get_target_model()
# Trace model
encoder_splits[0]_input_shape = encoder_splits[0]_model.get_input_spec()
encoder_splits[0]_sample_inputs = encoder_splits[0]_model.sample_inputs()

traced_encoder_splits[0]_model = torch.jit.trace(encoder_splits[0]_model, [torch.tensor(data[0]) for _, data in encoder_splits[0]_sample_inputs.items()])

# Compile model on a specific device
encoder_splits[0]_compile_job = hub.submit_compile_job(
    model=traced_encoder_splits[0]_model ,
    device=device,
    input_specs=encoder_splits[0]_model.get_input_spec(),
)

# Get target model to run on-device
encoder_splits[0]_target_model = encoder_splits[0]_compile_job.get_target_model()
# Trace model
encoder_splits[1]_input_shape = encoder_splits[1]_model.get_input_spec()
encoder_splits[1]_sample_inputs = encoder_splits[1]_model.sample_inputs()

traced_encoder_splits[1]_model = torch.jit.trace(encoder_splits[1]_model, [torch.tensor(data[0]) for _, data in encoder_splits[1]_sample_inputs.items()])

# Compile model on a specific device
encoder_splits[1]_compile_job = hub.submit_compile_job(
    model=traced_encoder_splits[1]_model ,
    device=device,
    input_specs=encoder_splits[1]_model.get_input_spec(),
)

# Get target model to run on-device
encoder_splits[1]_target_model = encoder_splits[1]_compile_job.get_target_model()
# Trace model
encoder_splits[2]_input_shape = encoder_splits[2]_model.get_input_spec()
encoder_splits[2]_sample_inputs = encoder_splits[2]_model.sample_inputs()

traced_encoder_splits[2]_model = torch.jit.trace(encoder_splits[2]_model, [torch.tensor(data[0]) for _, data in encoder_splits[2]_sample_inputs.items()])

# Compile model on a specific device
encoder_splits[2]_compile_job = hub.submit_compile_job(
    model=traced_encoder_splits[2]_model ,
    device=device,
    input_specs=encoder_splits[2]_model.get_input_spec(),
)

# Get target model to run on-device
encoder_splits[2]_target_model = encoder_splits[2]_compile_job.get_target_model()
# Trace model
encoder_splits[3]_input_shape = encoder_splits[3]_model.get_input_spec()
encoder_splits[3]_sample_inputs = encoder_splits[3]_model.sample_inputs()

traced_encoder_splits[3]_model = torch.jit.trace(encoder_splits[3]_model, [torch.tensor(data[0]) for _, data in encoder_splits[3]_sample_inputs.items()])

# Compile model on a specific device
encoder_splits[3]_compile_job = hub.submit_compile_job(
    model=traced_encoder_splits[3]_model ,
    device=device,
    input_specs=encoder_splits[3]_model.get_input_spec(),
)

# Get target model to run on-device
encoder_splits[3]_target_model = encoder_splits[3]_compile_job.get_target_model()
# Trace model
encoder_splits[4]_input_shape = encoder_splits[4]_model.get_input_spec()
encoder_splits[4]_sample_inputs = encoder_splits[4]_model.sample_inputs()

traced_encoder_splits[4]_model = torch.jit.trace(encoder_splits[4]_model, [torch.tensor(data[0]) for _, data in encoder_splits[4]_sample_inputs.items()])

# Compile model on a specific device
encoder_splits[4]_compile_job = hub.submit_compile_job(
    model=traced_encoder_splits[4]_model ,
    device=device,
    input_specs=encoder_splits[4]_model.get_input_spec(),
)

# Get target model to run on-device
encoder_splits[4]_target_model = encoder_splits[4]_compile_job.get_target_model()
# Trace model
encoder_splits[5]_input_shape = encoder_splits[5]_model.get_input_spec()
encoder_splits[5]_sample_inputs = encoder_splits[5]_model.sample_inputs()

traced_encoder_splits[5]_model = torch.jit.trace(encoder_splits[5]_model, [torch.tensor(data[0]) for _, data in encoder_splits[5]_sample_inputs.items()])

# Compile model on a specific device
encoder_splits[5]_compile_job = hub.submit_compile_job(
    model=traced_encoder_splits[5]_model ,
    device=device,
    input_specs=encoder_splits[5]_model.get_input_spec(),
)

# Get target model to run on-device
encoder_splits[5]_target_model = encoder_splits[5]_compile_job.get_target_model()

Step 2: Performance profiling on cloud-hosted device

After compiling models from step 1. Models can be profiled model on-device using the target_model. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics.

decoder_profile_job = hub.submit_profile_job(
    model=decoder_target_model,
    device=device,
)
encoder_splits[0]_profile_job = hub.submit_profile_job(
    model=encoder_splits[0]_target_model,
    device=device,
)
encoder_splits[1]_profile_job = hub.submit_profile_job(
    model=encoder_splits[1]_target_model,
    device=device,
)
encoder_splits[2]_profile_job = hub.submit_profile_job(
    model=encoder_splits[2]_target_model,
    device=device,
)
encoder_splits[3]_profile_job = hub.submit_profile_job(
    model=encoder_splits[3]_target_model,
    device=device,
)
encoder_splits[4]_profile_job = hub.submit_profile_job(
    model=encoder_splits[4]_target_model,
    device=device,
)
encoder_splits[5]_profile_job = hub.submit_profile_job(
    model=encoder_splits[5]_target_model,
    device=device,
)

Step 3: Verify on-device accuracy

To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device.

decoder_input_data = decoder_model.sample_inputs()
decoder_inference_job = hub.submit_inference_job(
    model=decoder_target_model,
    device=device,
    inputs=decoder_input_data,
)
decoder_inference_job.download_output_data()
encoder_splits[0]_input_data = encoder_splits[0]_model.sample_inputs()
encoder_splits[0]_inference_job = hub.submit_inference_job(
    model=encoder_splits[0]_target_model,
    device=device,
    inputs=encoder_splits[0]_input_data,
)
encoder_splits[0]_inference_job.download_output_data()
encoder_splits[1]_input_data = encoder_splits[1]_model.sample_inputs()
encoder_splits[1]_inference_job = hub.submit_inference_job(
    model=encoder_splits[1]_target_model,
    device=device,
    inputs=encoder_splits[1]_input_data,
)
encoder_splits[1]_inference_job.download_output_data()
encoder_splits[2]_input_data = encoder_splits[2]_model.sample_inputs()
encoder_splits[2]_inference_job = hub.submit_inference_job(
    model=encoder_splits[2]_target_model,
    device=device,
    inputs=encoder_splits[2]_input_data,
)
encoder_splits[2]_inference_job.download_output_data()
encoder_splits[3]_input_data = encoder_splits[3]_model.sample_inputs()
encoder_splits[3]_inference_job = hub.submit_inference_job(
    model=encoder_splits[3]_target_model,
    device=device,
    inputs=encoder_splits[3]_input_data,
)
encoder_splits[3]_inference_job.download_output_data()
encoder_splits[4]_input_data = encoder_splits[4]_model.sample_inputs()
encoder_splits[4]_inference_job = hub.submit_inference_job(
    model=encoder_splits[4]_target_model,
    device=device,
    inputs=encoder_splits[4]_input_data,
)
encoder_splits[4]_inference_job.download_output_data()
encoder_splits[5]_input_data = encoder_splits[5]_model.sample_inputs()
encoder_splits[5]_inference_job = hub.submit_inference_job(
    model=encoder_splits[5]_target_model,
    device=device,
    inputs=encoder_splits[5]_input_data,
)
encoder_splits[5]_inference_job.download_output_data()

With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output.

Note: This on-device profiling and inference requires access to Qualcomm® AI Hub. Sign up for access.

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.sam.demo --on-device

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.sam.demo -- --on-device

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on Segment-Anything-Model's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of Segment-Anything-Model can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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