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PSPNet

Image Segmentation
PyTorch
bu_auto
android
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v0.61.0

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See https://github.com/qualcomm/ai-hub-models/releases/v0.61.0 for changelog.

Files changed (2) hide show
  1. README.md +52 -52
  2. release_assets.json +4 -4
README.md CHANGED
@@ -1,6 +1,6 @@
1
  ---
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  library_name: pytorch
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- license: other
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  tags:
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  - bu_auto
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  - android
@@ -14,7 +14,7 @@ pipeline_tag: image-segmentation
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  PSPNet (Pyramid Scene Parsing Network) is a semantic segmentation model that captures global context information by applying pyramid pooling modules. It is designed to improve scene understanding by aggregating contextual features at multiple scales.
16
 
17
- This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.60.0/src/qai_hub_models/models/pspnet) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
18
 
19
  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.
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@@ -27,23 +27,23 @@ Below are pre-exported model assets ready for deployment.
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28
  | Runtime | Precision | Chipset | SDK Versions | Download |
29
  |---|---|---|---|---|
30
- | ONNX | float | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.60.0/pspnet-onnx-float.zip)
31
- | QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.60.0/pspnet-qnn_dlc-float.zip)
32
- | TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.60.0/pspnet-tflite-float.zip)
33
 
34
  For more device-specific assets and performance metrics, visit **[PSPNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/pspnet)**.
35
 
36
 
37
  ### Option 2: Export with Custom Configurations
38
 
39
- Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.60.0/src/qai_hub_models/models/pspnet) Python library to compile and export the model with your own:
40
  - Custom weights (e.g., fine-tuned checkpoints)
41
  - Custom input shapes
42
  - Target device and runtime configurations
43
 
44
  This option is ideal if you need to customize the model beyond the default configuration provided here.
45
 
46
- See our repository for [PSPNet on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.60.0/src/qai_hub_models/models/pspnet) for usage instructions.
47
 
48
  ## Model Details
49
 
@@ -58,51 +58,51 @@ See our repository for [PSPNet on GitHub](https://github.com/qualcomm/ai-hub-mod
58
  ## Performance Summary
59
  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
60
  |---|---|---|---|---|---|---
61
- | PSPNet | ONNX | float | Snapdragon® X2 Elite | 833.922 ms | 532 - 532 MB | NPU
62
- | PSPNet | ONNX | float | Snapdragon® X Elite | 1338.221 ms | 267 - 267 MB | NPU
63
- | PSPNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 956.163 ms | 207 - 2054 MB | NPU
64
- | PSPNet | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 2237.393 ms | 33 - 887 MB | NPU
65
- | PSPNet | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 1389.513 ms | 117 - 123 MB | NPU
66
- | PSPNet | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 1166.652 ms | 0 - 160 MB | NPU
67
- | PSPNet | ONNX | float | Qualcomm® QCS8450 | 2237.393 ms | 33 - 887 MB | NPU
68
- | PSPNet | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 1684.754 ms | 114 - 120 MB | NPU
69
- | PSPNet | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 1338.221 ms | 267 - 267 MB | NPU
70
- | PSPNet | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 648.933 ms | 170 - 1644 MB | NPU
71
- | PSPNet | ONNX | float | Snapdragon® 8 Elite Mobile | 648.933 ms | 170 - 1644 MB | NPU
72
- | PSPNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 717.737 ms | 117 - 1713 MB | NPU
73
- | PSPNet | QNN_DLC | float | Snapdragon® X2 Elite | 2505.903 ms | 3 - 3 MB | NPU
74
- | PSPNet | QNN_DLC | float | Snapdragon® X Elite | 2540.633 ms | 3 - 3 MB | NPU
75
- | PSPNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 1847.35 ms | 0 - 1654 MB | NPU
76
- | PSPNet | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 1609.246 ms | 0 - 852 MB | NPU
77
- | PSPNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 2531.233 ms | 3 - 136 MB | NPU
78
- | PSPNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 5289.358 ms | 0 - 1305 MB | NPU
79
- | PSPNet | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 2489.852 ms | 3 - 5 MB | NPU
80
- | PSPNet | QNN_DLC | float | Qualcomm® SA8775P | 2605.883 ms | 1 - 1307 MB | NPU
81
- | PSPNet | QNN_DLC | float | Qualcomm® SA8650P | 2605.883 ms | 1 - 1307 MB | NPU
82
- | PSPNet | QNN_DLC | float | Qualcomm® SA8255P | 2605.883 ms | 1 - 1307 MB | NPU
83
- | PSPNet | QNN_DLC | float | Qualcomm® QCS8450 | 1609.246 ms | 0 - 852 MB | NPU
84
- | PSPNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 2598.163 ms | 3 - 135 MB | NPU
85
- | PSPNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 2540.633 ms | 3 - 3 MB | NPU
86
- | PSPNet | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 2168.941 ms | 0 - 1310 MB | NPU
87
- | PSPNet | QNN_DLC | float | Qualcomm® SA7255P | 5289.358 ms | 0 - 1305 MB | NPU
88
- | PSPNet | QNN_DLC | float | Qualcomm® SA8295P | 1361.805 ms | 3 - 647 MB | NPU
89
- | PSPNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 2168.941 ms | 0 - 1310 MB | NPU
90
- | PSPNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 2341.485 ms | 0 - 1363 MB | NPU
91
- | PSPNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 2105.151 ms | 42 - 1747 MB | NPU
92
- | PSPNet | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 1861.665 ms | 129 - 1060 MB | NPU
93
- | PSPNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 2879.024 ms | 3 - 278 MB | NPU
94
- | PSPNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 5956.898 ms | 130 - 1528 MB | NPU
95
- | PSPNet | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 2841.025 ms | 0 - 4 MB | NPU
96
- | PSPNet | TFLITE | float | Qualcomm® SA8775P | 2954.21 ms | 123 - 1521 MB | NPU
97
- | PSPNet | TFLITE | float | Qualcomm® SA8650P | 2954.21 ms | 123 - 1521 MB | NPU
98
- | PSPNet | TFLITE | float | Qualcomm® SA8255P | 2954.21 ms | 123 - 1521 MB | NPU
99
- | PSPNet | TFLITE | float | Qualcomm® QCS8450 | 1861.665 ms | 129 - 1060 MB | NPU
100
- | PSPNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 2922.32 ms | 16 - 291 MB | NPU
101
- | PSPNet | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 2165.438 ms | 1 - 1409 MB | NPU
102
- | PSPNet | TFLITE | float | Qualcomm® SA7255P | 5956.898 ms | 130 - 1528 MB | NPU
103
- | PSPNet | TFLITE | float | Qualcomm® SA8295P | 1421.541 ms | 71 - 780 MB | NPU
104
- | PSPNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 2165.438 ms | 1 - 1409 MB | NPU
105
- | PSPNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 2329.54 ms | 5 - 1456 MB | NPU
106
 
107
  ## License
108
  * The license for the original implementation of PSPNet can be found
 
1
  ---
2
  library_name: pytorch
3
+ license: mit
4
  tags:
5
  - bu_auto
6
  - android
 
14
 
15
  PSPNet (Pyramid Scene Parsing Network) is a semantic segmentation model that captures global context information by applying pyramid pooling modules. It is designed to improve scene understanding by aggregating contextual features at multiple scales.
16
 
17
+ This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.61.0/src/qai_hub_models/models/pspnet) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
18
 
19
  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.
20
 
 
27
 
28
  | Runtime | Precision | Chipset | SDK Versions | Download |
29
  |---|---|---|---|---|
30
+ | ONNX | float | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.61.0/pspnet-onnx-float.zip)
31
+ | QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.61.0/pspnet-qnn_dlc-float.zip)
32
+ | TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.61.0/pspnet-tflite-float.zip)
33
 
34
  For more device-specific assets and performance metrics, visit **[PSPNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/pspnet)**.
35
 
36
 
37
  ### Option 2: Export with Custom Configurations
38
 
39
+ Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.61.0/src/qai_hub_models/models/pspnet) Python library to compile and export the model with your own:
40
  - Custom weights (e.g., fine-tuned checkpoints)
41
  - Custom input shapes
42
  - Target device and runtime configurations
43
 
44
  This option is ideal if you need to customize the model beyond the default configuration provided here.
45
 
46
+ See our repository for [PSPNet on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.61.0/src/qai_hub_models/models/pspnet) for usage instructions.
47
 
48
  ## Model Details
49
 
 
58
  ## Performance Summary
59
  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
60
  |---|---|---|---|---|---|---
61
+ | PSPNet | ONNX | float | Snapdragon® X2 Elite | 832.612 ms | 528 - 528 MB | NPU
62
+ | PSPNet | ONNX | float | Snapdragon® X Elite | 1335.437 ms | 267 - 267 MB | NPU
63
+ | PSPNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 954.711 ms | 0 - 1844 MB | NPU
64
+ | PSPNet | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 2247.017 ms | 47 - 904 MB | NPU
65
+ | PSPNet | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 1394.431 ms | 14 - 20 MB | NPU
66
+ | PSPNet | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 1143.919 ms | 0 - 160 MB | NPU
67
+ | PSPNet | ONNX | float | Qualcomm® QCS8450 | 2247.017 ms | 47 - 904 MB | NPU
68
+ | PSPNet | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 1418.156 ms | 8 - 13 MB | NPU
69
+ | PSPNet | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 1335.437 ms | 267 - 267 MB | NPU
70
+ | PSPNet | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 651.51 ms | 119 - 1591 MB | NPU
71
+ | PSPNet | ONNX | float | Snapdragon® 8 Elite Mobile | 651.51 ms | 119 - 1591 MB | NPU
72
+ | PSPNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 718.505 ms | 135 - 1731 MB | NPU
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+ | PSPNet | QNN_DLC | float | Snapdragon® X2 Elite | 2495.773 ms | 3 - 3 MB | NPU
74
+ | PSPNet | QNN_DLC | float | Snapdragon® X Elite | 2538.555 ms | 3 - 3 MB | NPU
75
+ | PSPNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 1837.963 ms | 55 - 1708 MB | NPU
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+ | PSPNet | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 1601.931 ms | 1 - 851 MB | NPU
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+ | PSPNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 2523.411 ms | 3 - 136 MB | NPU
78
+ | PSPNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 5283.318 ms | 2 - 1307 MB | NPU
79
+ | PSPNet | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 2494.466 ms | 3 - 947 MB | NPU
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+ | PSPNet | QNN_DLC | float | Qualcomm® SA8775P | 2615.805 ms | 2 - 1307 MB | NPU
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+ | PSPNet | QNN_DLC | float | Qualcomm® SA8650P | 2615.805 ms | 2 - 1307 MB | NPU
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+ | PSPNet | QNN_DLC | float | Qualcomm® SA8255P | 2615.805 ms | 2 - 1307 MB | NPU
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+ | PSPNet | QNN_DLC | float | Qualcomm® QCS8450 | 1601.931 ms | 1 - 851 MB | NPU
84
+ | PSPNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 3340.187 ms | 5 - 137 MB | NPU
85
+ | PSPNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 2538.555 ms | 3 - 3 MB | NPU
86
+ | PSPNet | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 2168.014 ms | 0 - 1311 MB | NPU
87
+ | PSPNet | QNN_DLC | float | Qualcomm® SA7255P | 5283.318 ms | 2 - 1307 MB | NPU
88
+ | PSPNet | QNN_DLC | float | Qualcomm® SA8295P | 1365.366 ms | 3 - 647 MB | NPU
89
+ | PSPNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 2168.014 ms | 0 - 1311 MB | NPU
90
+ | PSPNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 2328.585 ms | 0 - 1364 MB | NPU
91
+ | PSPNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 2112.072 ms | 127 - 1832 MB | NPU
92
+ | PSPNet | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 1877.964 ms | 24 - 953 MB | NPU
93
+ | PSPNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 2876.19 ms | 0 - 276 MB | NPU
94
+ | PSPNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 5954.347 ms | 103 - 1502 MB | NPU
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+ | PSPNet | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 2811.238 ms | 38 - 42 MB | NPU
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+ | PSPNet | TFLITE | float | Qualcomm® SA8775P | 2961.091 ms | 108 - 1506 MB | NPU
97
+ | PSPNet | TFLITE | float | Qualcomm® SA8650P | 2961.091 ms | 108 - 1506 MB | NPU
98
+ | PSPNet | TFLITE | float | Qualcomm® SA8255P | 2961.091 ms | 108 - 1506 MB | NPU
99
+ | PSPNet | TFLITE | float | Qualcomm® QCS8450 | 1877.964 ms | 24 - 953 MB | NPU
100
+ | PSPNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 2933.881 ms | 108 - 383 MB | NPU
101
+ | PSPNet | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 2168.575 ms | 1 - 1408 MB | NPU
102
+ | PSPNet | TFLITE | float | Qualcomm® SA7255P | 5954.347 ms | 103 - 1502 MB | NPU
103
+ | PSPNet | TFLITE | float | Qualcomm® SA8295P | 1418.676 ms | 125 - 835 MB | NPU
104
+ | PSPNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 2168.575 ms | 1 - 1408 MB | NPU
105
+ | PSPNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 2324.393 ms | 0 - 1452 MB | NPU
106
 
107
  ## License
108
  * The license for the original implementation of PSPNet can be found
release_assets.json CHANGED
@@ -1,5 +1,5 @@
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  {
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- "version": "0.60.0",
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  "precisions": {
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  "float": {
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  "universal_assets": {
@@ -8,19 +8,19 @@
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  "qairt": "2.45.0.260326154327",
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  "onnx_runtime": "1.27.1"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.60.0/pspnet-onnx-float.zip"
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  },
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  "qnn_dlc": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.60.0/pspnet-qnn_dlc-float.zip"
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  },
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  "tflite": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327"
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  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.60.0/pspnet-tflite-float.zip"
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  }
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  }
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  }
 
1
  {
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+ "version": "0.61.0",
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  "precisions": {
4
  "float": {
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  "universal_assets": {
 
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  "qairt": "2.45.0.260326154327",
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  "onnx_runtime": "1.27.1"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.61.0/pspnet-onnx-float.zip"
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  },
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  "qnn_dlc": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.61.0/pspnet-qnn_dlc-float.zip"
18
  },
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  "tflite": {
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  "tool_versions": {
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  "qairt": "2.45.0.260326154327"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.61.0/pspnet-tflite-float.zip"
24
  }
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  }
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  }