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qualcomm
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LiteHRNet

Keypoint Detection
PyTorch
LiteRT
android
Model card Files Community
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v0.30.5

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See https://github.com/quic/ai-hub-models/releases/v0.30.5 for changelog.

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  1. README.md +20 -20
README.md CHANGED
@@ -33,22 +33,22 @@ More details on model performance across various devices, can be found
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  | Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
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  |---|---|---|---|---|---|---|---|---|
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- | LiteHRNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 8.964 ms | 0 - 51 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 5.419 ms | 0 - 63 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 4.582 ms | 0 - 15 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 5.656 ms | 0 - 52 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 8.964 ms | 0 - 51 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 4.597 ms | 0 - 18 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 6.413 ms | 0 - 54 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 4.635 ms | 0 - 17 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 5.656 ms | 0 - 52 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 4.608 ms | 0 - 15 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 7.537 ms | 0 - 24 MB | NPU | [LiteHRNet.onnx](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx) |
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- | LiteHRNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 2.853 ms | 0 - 62 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 4.494 ms | 1 - 59 MB | NPU | [LiteHRNet.onnx](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx) |
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- | LiteHRNet | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 2.871 ms | 0 - 52 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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- | LiteHRNet | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 4.044 ms | 1 - 52 MB | NPU | [LiteHRNet.onnx](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx) |
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- | LiteHRNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 8.008 ms | 4 - 4 MB | NPU | [LiteHRNet.onnx](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx) |
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@@ -112,8 +112,8 @@ Profiling Results
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  LiteHRNet
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  Device : cs_8275 (ANDROID 14)
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  Runtime : TFLITE
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- Estimated inference time (ms) : 9.0
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- Estimated peak memory usage (MB): [0, 51]
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  Total # Ops : 1113
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  Compute Unit(s) : npu (1111 ops) gpu (0 ops) cpu (2 ops)
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  ```
@@ -202,13 +202,13 @@ AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
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  You can also run the demo on-device.
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  ```bash
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- python -m qai_hub_models.models.litehrnet.demo --on-device
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  ```
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  **NOTE**: If you want running in a Jupyter Notebook or Google Colab like
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  environment, please add the following to your cell (instead of the above).
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  ```
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- %run -m qai_hub_models.models.litehrnet.demo -- --on-device
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  ```
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  | Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
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  |---|---|---|---|---|---|---|---|---|
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+ | LiteHRNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 24.057 ms | 0 - 65 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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+ | LiteHRNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 5.303 ms | 0 - 67 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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+ | LiteHRNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 4.608 ms | 0 - 23 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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+ | LiteHRNet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 5.598 ms | 0 - 64 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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+ | LiteHRNet | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 24.057 ms | 0 - 65 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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+ | LiteHRNet | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 4.6 ms | 0 - 19 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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+ | LiteHRNet | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 6.528 ms | 0 - 62 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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+ | LiteHRNet | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 4.629 ms | 0 - 23 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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+ | LiteHRNet | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 5.598 ms | 0 - 64 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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+ | LiteHRNet | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 4.634 ms | 0 - 20 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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+ | LiteHRNet | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 5.998 ms | 0 - 23 MB | NPU | [LiteHRNet.onnx](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx) |
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+ | LiteHRNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 2.768 ms | 0 - 71 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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+ | LiteHRNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 3.749 ms | 0 - 57 MB | NPU | [LiteHRNet.onnx](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx) |
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+ | LiteHRNet | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 2.359 ms | 0 - 64 MB | NPU | [LiteHRNet.tflite](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.tflite) |
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+ | LiteHRNet | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 4.014 ms | 0 - 52 MB | NPU | [LiteHRNet.onnx](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx) |
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+ | LiteHRNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 6.494 ms | 4 - 4 MB | NPU | [LiteHRNet.onnx](https://huggingface.co/qualcomm/LiteHRNet/blob/main/LiteHRNet.onnx) |
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  LiteHRNet
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  Device : cs_8275 (ANDROID 14)
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  Runtime : TFLITE
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+ Estimated inference time (ms) : 24.1
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+ Estimated peak memory usage (MB): [0, 65]
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  Total # Ops : 1113
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  Compute Unit(s) : npu (1111 ops) gpu (0 ops) cpu (2 ops)
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  ```
 
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  You can also run the demo on-device.
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  ```bash
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+ python -m qai_hub_models.models.litehrnet.demo --eval-mode on-device
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  ```
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  **NOTE**: If you want running in a Jupyter Notebook or Google Colab like
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  environment, please add the following to your cell (instead of the above).
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  ```
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+ %run -m qai_hub_models.models.litehrnet.demo -- --eval-mode on-device
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  ```
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