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LiteHRNet

Keypoint Detection
PyTorch
android
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v0.63.0

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See https://github.com/qualcomm/ai-hub-models/releases/v0.63.0 for changelog.

Files changed (2) hide show
  1. README.md +45 -49
  2. release_assets.json +7 -7
README.md CHANGED
@@ -14,7 +14,7 @@ pipeline_tag: keypoint-detection
14
  LiteHRNet is a machine learning model that detects human pose and returns a location and confidence for each of 17 joints.
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16
  This is based on the implementation of LiteHRNet found [here](https://github.com/HRNet/Lite-HRNet).
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/v0.62.2/src/qai_hub_models/models/litehrnet) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
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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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  | 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/litehrnet/releases/v0.62.2/litehrnet-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/litehrnet/releases/v0.62.2/litehrnet-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/litehrnet/releases/v0.62.2/litehrnet-tflite-float.zip)
33
 
34
  For more device-specific assets and performance metrics, visit **[LiteHRNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/litehrnet)**.
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/v0.62.2/src/qai_hub_models/models/litehrnet) 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 [LiteHRNet on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.62.2/src/qai_hub_models/models/litehrnet) for usage instructions.
47
 
48
  ## Model Details
49
 
@@ -57,49 +57,45 @@ See our repository for [LiteHRNet on GitHub](https://github.com/qualcomm/ai-hub-
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  ## Performance Summary
58
  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
59
  |---|---|---|---|---|---|---
60
- | LiteHRNet | ONNX | float | Snapdragon® X2 Elite | 2.876 ms | 2 - 2 MB | NPU
61
- | LiteHRNet | ONNX | float | Snapdragon® X Elite | 5.727 ms | 5 - 5 MB | NPU
62
- | LiteHRNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 3.101 ms | 0 - 121 MB | NPU
63
- | LiteHRNet | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 6.297 ms | 1 - 121 MB | NPU
64
- | LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 4.485 ms | 1 - 5 MB | NPU
65
- | LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5.427 ms | 1 - 3 MB | NPU
66
- | LiteHRNet | ONNX | float | Qualcomm® QCS8450 | 6.297 ms | 1 - 121 MB | NPU
67
- | LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 5.763 ms | 1 - 4 MB | NPU
68
- | LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 5.727 ms | 5 - 5 MB | NPU
69
- | LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 2.887 ms | 0 - 98 MB | NPU
70
- | LiteHRNet | ONNX | float | Snapdragon® 8 Elite Mobile | 2.887 ms | 0 - 98 MB | NPU
71
- | LiteHRNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.778 ms | 1 - 99 MB | NPU
72
- | LiteHRNet | QNN_DLC | float | Snapdragon® X2 Elite | 1.298 ms | 1 - 1 MB | NPU
73
- | LiteHRNet | QNN_DLC | float | Snapdragon® X Elite | 2.458 ms | 1 - 1 MB | NPU
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- | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 1.39 ms | 0 - 103 MB | NPU
75
- | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 2.954 ms | 0 - 101 MB | NPU
76
- | LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 2.189 ms | 1 - 4 MB | NPU
77
- | LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 2.11 ms | 1 - 2 MB | NPU
78
- | LiteHRNet | QNN_DLC | float | Qualcomm® SA8775P | 2.675 ms | 0 - 80 MB | NPU
79
- | LiteHRNet | QNN_DLC | float | Qualcomm® SA8650P | 2.675 ms | 0 - 80 MB | NPU
80
- | LiteHRNet | QNN_DLC | float | Qualcomm® SA8255P | 2.675 ms | 0 - 80 MB | NPU
81
- | LiteHRNet | QNN_DLC | float | Qualcomm® QCS8450 | 2.954 ms | 0 - 101 MB | NPU
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- | LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 2.503 ms | 3 - 5 MB | NPU
83
- | LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 2.458 ms | 1 - 1 MB | NPU
84
- | LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 1.08 ms | 1 - 82 MB | NPU
85
- | LiteHRNet | QNN_DLC | float | Qualcomm® SA7255P | 5.028 ms | 1 - 78 MB | NPU
86
- | LiteHRNet | QNN_DLC | float | Qualcomm® SA8295P | 3.477 ms | 0 - 81 MB | NPU
87
- | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 1.08 ms | 1 - 82 MB | NPU
88
- | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.908 ms | 1 - 84 MB | NPU
89
- | LiteHRNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 2.675 ms | 0 - 147 MB | NPU
90
- | LiteHRNet | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 5.345 ms | 1 - 138 MB | NPU
91
- | LiteHRNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 4.223 ms | 1 - 12 MB | NPU
92
- | LiteHRNet | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 4.23 ms | 0 - 3 MB | NPU
93
- | LiteHRNet | TFLITE | float | Qualcomm® SA8775P | 5.391 ms | 1 - 116 MB | NPU
94
- | LiteHRNet | TFLITE | float | Qualcomm® SA8650P | 5.391 ms | 1 - 116 MB | NPU
95
- | LiteHRNet | TFLITE | float | Qualcomm® SA8255P | 5.391 ms | 1 - 116 MB | NPU
96
- | LiteHRNet | TFLITE | float | Qualcomm® QCS8450 | 5.345 ms | 1 - 138 MB | NPU
97
- | LiteHRNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 4.906 ms | 1 - 11 MB | NPU
98
- | LiteHRNet | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 2.254 ms | 0 - 119 MB | NPU
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- | LiteHRNet | TFLITE | float | Qualcomm® SA7255P | 8.832 ms | 1 - 116 MB | NPU
100
- | LiteHRNet | TFLITE | float | Qualcomm® SA8295P | 6.408 ms | 1 - 113 MB | NPU
101
- | LiteHRNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 2.254 ms | 0 - 119 MB | NPU
102
- | LiteHRNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.053 ms | 0 - 109 MB | NPU
103
 
104
  ## License
105
  * The license for the original implementation of LiteHRNet can be found
 
14
  LiteHRNet is a machine learning model that detects human pose and returns a location and confidence for each of 17 joints.
15
 
16
  This is based on the implementation of LiteHRNet found [here](https://github.com/HRNet/Lite-HRNet).
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/v0.63.0/src/qai_hub_models/models/litehrnet) 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 2.50, ONNX Runtime 1.27.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.63.0/litehrnet-onnx-float.zip)
31
+ | QNN_DLC | float | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.63.0/litehrnet-qnn_dlc-float.zip)
32
+ | TFLITE | float | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.63.0/litehrnet-tflite-float.zip)
33
 
34
  For more device-specific assets and performance metrics, visit **[LiteHRNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/litehrnet)**.
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/v0.63.0/src/qai_hub_models/models/litehrnet) 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 [LiteHRNet on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.63.0/src/qai_hub_models/models/litehrnet) for usage instructions.
47
 
48
  ## Model Details
49
 
 
57
  ## Performance Summary
58
  | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
59
  |---|---|---|---|---|---|---
60
+ | LiteHRNet | ONNX | float | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 2.505 ms | 0 - 102 MB | NPU
61
+ | LiteHRNet | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 2.669 ms | 0 - 100 MB | NPU
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+ | LiteHRNet | ONNX | float | Snapdragon® X2 Elite | 2.61 ms | 2 - 2 MB | NPU
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+ | LiteHRNet | ONNX | float | Snapdragon® X Elite | 5.636 ms | 5 - 5 MB | NPU
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+ | LiteHRNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 3.029 ms | 0 - 126 MB | NPU
65
+ | LiteHRNet | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 6.298 ms | 1 - 122 MB | NPU
66
+ | LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 4.375 ms | 1 - 5 MB | NPU
67
+ | LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5.335 ms | 0 - 8 MB | NPU
68
+ | LiteHRNet | ONNX | float | Qualcomm® QCS8450 | 6.298 ms | 1 - 122 MB | NPU
69
+ | LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 5.768 ms | 1 - 4 MB | NPU
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+ | LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 5.636 ms | 5 - 5 MB | NPU
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+ | LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 2.669 ms | 0 - 100 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 0.902 ms | 1 - 85 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 1.072 ms | 1 - 83 MB | NPU
74
+ | LiteHRNet | QNN_DLC | float | Snapdragon® X2 Elite | 1.335 ms | 1 - 1 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Snapdragon® X Elite | 2.407 ms | 1 - 1 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 1.403 ms | 0 - 99 MB | NPU
77
+ | LiteHRNet | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 2.887 ms | 1 - 101 MB | NPU
78
+ | LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 2.171 ms | 1 - 4 MB | NPU
79
+ | LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 2.093 ms | 1 - 3 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Qualcomm® QCS8450 | 2.887 ms | 1 - 101 MB | NPU
81
+ | LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 2.478 ms | 3 - 5 MB | NPU
82
+ | LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 2.407 ms | 1 - 1 MB | NPU
83
+ | LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 1.072 ms | 1 - 83 MB | NPU
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+ | LiteHRNet | QNN_DLC | float | Qualcomm® SA8295P | 3.345 ms | 0 - 81 MB | NPU
85
+ | LiteHRNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 2.057 ms | 0 - 110 MB | NPU
86
+ | LiteHRNet | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 2.229 ms | 0 - 114 MB | NPU
87
+ | LiteHRNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 2.672 ms | 0 - 146 MB | NPU
88
+ | LiteHRNet | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 5.209 ms | 1 - 135 MB | NPU
89
+ | LiteHRNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 4.204 ms | 1 - 12 MB | NPU
90
+ | LiteHRNet | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 4.227 ms | 0 - 3 MB | NPU
91
+ | LiteHRNet | TFLITE | float | Qualcomm® SA8775P | 5.278 ms | 1 - 112 MB | NPU
92
+ | LiteHRNet | TFLITE | float | Qualcomm® SA8650P | 5.278 ms | 1 - 112 MB | NPU
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+ | LiteHRNet | TFLITE | float | Qualcomm® SA8255P | 5.278 ms | 1 - 112 MB | NPU
94
+ | LiteHRNet | TFLITE | float | Qualcomm® QCS8450 | 5.209 ms | 1 - 135 MB | NPU
95
+ | LiteHRNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 4.918 ms | 1 - 12 MB | NPU
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+ | LiteHRNet | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 2.229 ms | 0 - 114 MB | NPU
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+ | LiteHRNet | TFLITE | float | Qualcomm® SA7255P | 8.598 ms | 1 - 111 MB | NPU
98
+ | LiteHRNet | TFLITE | float | Qualcomm® SA8295P | 6.016 ms | 1 - 111 MB | NPU
 
 
 
 
99
 
100
  ## License
101
  * The license for the original implementation of LiteHRNet can be found
release_assets.json CHANGED
@@ -1,26 +1,26 @@
1
  {
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- "version": "0.62.2",
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  "precisions": {
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  "float": {
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  "universal_assets": {
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  "onnx": {
7
  "tool_versions": {
8
- "qairt": "2.45.0.260326154327",
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  "onnx_runtime": "1.27.1"
10
  },
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- "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.62.2/litehrnet-onnx-float.zip"
12
  },
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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/litehrnet/releases/v0.62.2/litehrnet-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/litehrnet/releases/v0.62.2/litehrnet-tflite-float.zip"
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  }
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  }
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  }
 
1
  {
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+ "version": "0.63.0",
3
  "precisions": {
4
  "float": {
5
  "universal_assets": {
6
  "onnx": {
7
  "tool_versions": {
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+ "qairt": "2.50.0.260828221209",
9
  "onnx_runtime": "1.27.1"
10
  },
11
+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.63.0/litehrnet-onnx-float.zip"
12
  },
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  "qnn_dlc": {
14
  "tool_versions": {
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+ "qairt": "2.50.0.260828221209"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.63.0/litehrnet-qnn_dlc-float.zip"
18
  },
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  "tflite": {
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  "tool_versions": {
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+ "qairt": "2.50.0.260828221209"
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  },
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+ "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.63.0/litehrnet-tflite-float.zip"
24
  }
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  }
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  }