File size: 7,553 Bytes
dd2bca6 39e2973 dd2bca6 d898146 dd2bca6 267f725 dd2bca6 076d7ea 7e93f2f dd2bca6 076d7ea 5ed8b5f 076d7ea 5ed8b5f 076d7ea 5ed8b5f 076d7ea 5ed8b5f 076d7ea 5ed8b5f 076d7ea 5ed8b5f 6779b96 dd2bca6 1ea3cba 6779b96 dd2bca6 5ddcaf5 dd2bca6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | ---
library_name: pytorch
license: other
tags:
- android
pipeline_tag: keypoint-detection
---
# LiteHRNet: Optimized for Qualcomm Devices
LiteHRNet is a machine learning model that detects human pose and returns a location and confidence for each of 17 joints.
This is based on the implementation of LiteHRNet found [here](https://github.com/HRNet/Lite-HRNet).
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/litehrnet) 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 2.45, ONNX Runtime 1.27.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.60.0/litehrnet-onnx-float.zip)
| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.60.0/litehrnet-qnn_dlc-float.zip)
| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/litehrnet/releases/v0.60.0/litehrnet-tflite-float.zip)
For more device-specific assets and performance metrics, visit **[LiteHRNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/litehrnet)**.
### Option 2: Export with Custom Configurations
Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.60.0/src/qai_hub_models/models/litehrnet) 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 [LiteHRNet on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.60.0/src/qai_hub_models/models/litehrnet) for usage instructions.
## Model Details
**Model Type:** Model_use_case.pose_estimation
**Model Stats:**
- Input resolution: 256x192
- Model size (float): 4.49 MB
- Number of parameters: 1.11M
## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| LiteHRNet | ONNX | float | Snapdragon® X2 Elite | 2.884 ms | 2 - 2 MB | NPU
| LiteHRNet | ONNX | float | Snapdragon® X Elite | 5.743 ms | 6 - 6 MB | NPU
| LiteHRNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 3.115 ms | 0 - 124 MB | NPU
| LiteHRNet | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 6.302 ms | 1 - 121 MB | NPU
| LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 4.469 ms | 1 - 5 MB | NPU
| LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 5.42 ms | 1 - 22 MB | NPU
| LiteHRNet | ONNX | float | Qualcomm® QCS8450 | 6.302 ms | 1 - 121 MB | NPU
| LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 5.767 ms | 1 - 4 MB | NPU
| LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 5.743 ms | 6 - 6 MB | NPU
| LiteHRNet | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 2.875 ms | 0 - 100 MB | NPU
| LiteHRNet | ONNX | float | Snapdragon® 8 Elite Mobile | 2.875 ms | 0 - 100 MB | NPU
| LiteHRNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.782 ms | 0 - 100 MB | NPU
| LiteHRNet | QNN_DLC | float | Snapdragon® X2 Elite | 1.351 ms | 1 - 1 MB | NPU
| LiteHRNet | QNN_DLC | float | Snapdragon® X Elite | 2.417 ms | 1 - 1 MB | NPU
| LiteHRNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 1.404 ms | 0 - 104 MB | NPU
| LiteHRNet | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 2.955 ms | 0 - 102 MB | NPU
| LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 2.197 ms | 1 - 4 MB | NPU
| LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 5.061 ms | 1 - 78 MB | NPU
| LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 2.12 ms | 1 - 94 MB | NPU
| LiteHRNet | QNN_DLC | float | Qualcomm® SA8775P | 2.707 ms | 1 - 78 MB | NPU
| LiteHRNet | QNN_DLC | float | Qualcomm® SA8650P | 2.707 ms | 1 - 78 MB | NPU
| LiteHRNet | QNN_DLC | float | Qualcomm® SA8255P | 2.707 ms | 1 - 78 MB | NPU
| LiteHRNet | QNN_DLC | float | Qualcomm® QCS8450 | 2.955 ms | 0 - 102 MB | NPU
| LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 2.522 ms | 1 - 3 MB | NPU
| LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 2.417 ms | 1 - 1 MB | NPU
| LiteHRNet | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 1.077 ms | 0 - 81 MB | NPU
| LiteHRNet | QNN_DLC | float | Qualcomm® SA7255P | 5.061 ms | 1 - 78 MB | NPU
| LiteHRNet | QNN_DLC | float | Qualcomm® SA8295P | 3.494 ms | 0 - 82 MB | NPU
| LiteHRNet | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 1.077 ms | 0 - 81 MB | NPU
| LiteHRNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 0.916 ms | 1 - 84 MB | NPU
| LiteHRNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 2.688 ms | 0 - 147 MB | NPU
| LiteHRNet | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 5.361 ms | 1 - 138 MB | NPU
| LiteHRNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 4.216 ms | 1 - 12 MB | NPU
| LiteHRNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 8.85 ms | 1 - 116 MB | NPU
| LiteHRNet | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 4.227 ms | 0 - 2 MB | NPU
| LiteHRNet | TFLITE | float | Qualcomm® SA8775P | 5.364 ms | 1 - 116 MB | NPU
| LiteHRNet | TFLITE | float | Qualcomm® SA8650P | 5.364 ms | 1 - 116 MB | NPU
| LiteHRNet | TFLITE | float | Qualcomm® SA8255P | 5.364 ms | 1 - 116 MB | NPU
| LiteHRNet | TFLITE | float | Qualcomm® QCS8450 | 5.361 ms | 1 - 138 MB | NPU
| LiteHRNet | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 4.911 ms | 1 - 11 MB | NPU
| LiteHRNet | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 2.245 ms | 0 - 117 MB | NPU
| LiteHRNet | TFLITE | float | Qualcomm® SA7255P | 8.85 ms | 1 - 116 MB | NPU
| LiteHRNet | TFLITE | float | Qualcomm® SA8295P | 6.333 ms | 1 - 113 MB | NPU
| LiteHRNet | TFLITE | float | Snapdragon® 8 Elite Mobile | 2.245 ms | 0 - 117 MB | NPU
| LiteHRNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.043 ms | 0 - 111 MB | NPU
## License
* The license for the original implementation of LiteHRNet can be found
[here](https://github.com/HRNet/Lite-HRNet/blob/hrnet/LICENSE).
## References
* [Lite-HRNet: A Lightweight High-Resolution Network](https://arxiv.org/abs/2104.06403)
* [Source Model Implementation](https://github.com/HRNet/Lite-HRNet)
## 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).
|