v0.46.1

See https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.

README.md

@@ -9,234 +9,95 @@ pipeline_tag: keypoint-detection
 
 ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/web-assets/model_demo.png)
 
-# CenterNet-Pose: Optimized for Mobile Deployment
-## Human pose estimation
-
+# CenterNet-Pose: Optimized for Qualcomm Devices
 
 CenterNet-Pose is a machine learning model that detects human pose and returns a location and confidence for each of 17 joints.
 
-This model is an implementation of CenterNet-Pose found [here](https://github.com/xingyizhou/CenterNet).
-
-
-This repository provides scripts to run CenterNet-Pose on Qualcomm® devices.
-More details on model performance across various devices, can be found
-[here](https://aihub.qualcomm.com/models/centernet_pose).
-
-
-
-### Model Details
-
-- **Model Type:** Model_use_case.pose_estimation
-- **Model Stats:**
-  - Model checkpoint: multi_pose_dla_3x.pth
-  - Input resolution: 1 x 3 x 512 x 512
-  - Number of parameters: 20.6M
-  - Model size: 57.8 MB
-
-| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
-|---|---|---|---|---|---|---|---|---|
-| CenterNet-Pose | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_CONTEXT_BINARY | 103.893 ms | 1 - 10 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_CONTEXT_BINARY | 80.654 ms | 1 - 21 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_CONTEXT_BINARY | 53.778 ms | 1 - 3 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | PRECOMPILED_QNN_ONNX | 53.93 ms | 0 - 49 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_CONTEXT_BINARY | 56.327 ms | 1 - 10 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | SA7255P ADP | Qualcomm® SA7255P | QNN_CONTEXT_BINARY | 103.893 ms | 1 - 10 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | SA8295P ADP | Qualcomm® SA8295P | QNN_CONTEXT_BINARY | 70.476 ms | 1 - 15 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | SA8775P ADP | Qualcomm® SA8775P | QNN_CONTEXT_BINARY | 56.327 ms | 1 - 10 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_CONTEXT_BINARY | 40.207 ms | 1 - 19 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | PRECOMPILED_QNN_ONNX | 39.089 ms | 3 - 23 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_CONTEXT_BINARY | 29.551 ms | 1 - 13 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | PRECOMPILED_QNN_ONNX | 32.027 ms | 3 - 17 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_CONTEXT_BINARY | 26.691 ms | 1 - 12 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | PRECOMPILED_QNN_ONNX | 28.111 ms | 3 - 14 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_CONTEXT_BINARY | 55.485 ms | 1 - 1 MB | NPU | Use Export Script |
-| CenterNet-Pose | float | Snapdragon X Elite CRD | Snapdragon® X Elite | PRECOMPILED_QNN_ONNX | 55.471 ms | 44 - 44 MB | NPU | Use Export Script |
-
-
-
-
-## Installation
-
-
-Install the package via pip:
-```bash
-pip install qai-hub-models
-```
-
-
-## Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device
-
-Sign-in to [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.com/) 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.
-```bash
-qai-hub configure --api_token API_TOKEN
-```
-Navigate to [docs](https://workbench.aihub.qualcomm.com/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.
-
-```bash
-python -m qai_hub_models.models.centernet_pose.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.centernet_pose.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.
-
-```bash
-python -m qai_hub_models.models.centernet_pose.export
-```
-
-
-
-## How does this work?
-
-This [export script](https://aihub.qualcomm.com/models/centernet_pose/qai_hub_models/models/CenterNet-Pose/export.py)
-leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) 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.
-
-```python
-import torch
-
-import qai_hub as hub
-from qai_hub_models.models.centernet_pose import Model
-
-# Load the model
-torch_model = Model.from_pretrained()
-
-# Device
-device = hub.Device("Samsung Galaxy S25")
-
-# Trace model
-input_shape = torch_model.get_input_spec()
-sample_inputs = torch_model.sample_inputs()
-
-pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
-
-# Compile model on a specific device
-compile_job = hub.submit_compile_job(
-    model=pt_model,
-    device=device,
-    input_specs=torch_model.get_input_spec(),
-)
-
-# Get target model to run on-device
-target_model = 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.
-```python
-profile_job = hub.submit_profile_job(
-    model=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.
-```python
-input_data = torch_model.sample_inputs()
-inference_job = hub.submit_inference_job(
-    model=target_model,
-    device=device,
-    inputs=input_data,
-)
-    on_device_output = 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 Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
-
-
-
-## Run demo on a cloud-hosted device
-
-You can also run the demo on-device.
-
-```bash
-python -m qai_hub_models.models.centernet_pose.demo --eval-mode 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.centernet_pose.demo -- --eval-mode on-device
-```
-
-
-## Deploying compiled model to Android
-
-
-The models can be deployed using multiple runtimes:
-- TensorFlow Lite (`.tflite` export): [This
-  tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
-  guide to deploy the .tflite model in an Android application.
-
-
-- QNN (`.so` export ): This [sample
-  app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
-provides instructions on how to use the `.so` shared library  in an Android application.
-
-
-## View on Qualcomm® AI Hub
-Get more details on CenterNet-Pose's performance across various devices [here](https://aihub.qualcomm.com/models/centernet_pose).
-Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
-
+This is based on the implementation of CenterNet-Pose found [here](https://github.com/xingyizhou/CenterNet).
+This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/centernet_pose) 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 |
+|---|---|---|---|---|
+| PRECOMPILED_QNN_ONNX | float | Snapdragon® X Elite | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-precompiled_qnn_onnx-float-qualcomm_snapdragon_x_elite.zip)
+| PRECOMPILED_QNN_ONNX | float | Snapdragon® 8 Gen 3 Mobile | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-precompiled_qnn_onnx-float-qualcomm_snapdragon_8gen3.zip)
+| PRECOMPILED_QNN_ONNX | float | Qualcomm® QCS8550 (Proxy) | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-precompiled_qnn_onnx-float-qualcomm_qcs8550_proxy.zip)
+| PRECOMPILED_QNN_ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-precompiled_qnn_onnx-float-qualcomm_snapdragon_8_elite_for_galaxy.zip)
+| PRECOMPILED_QNN_ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-precompiled_qnn_onnx-float-qualcomm_snapdragon_8_elite_gen5.zip)
+| PRECOMPILED_QNN_ONNX | float | Qualcomm® QCS9075 | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-precompiled_qnn_onnx-float-qualcomm_qcs9075.zip)
+| QNN_CONTEXT_BINARY | float | Snapdragon® X Elite | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-qnn_context_binary-float-qualcomm_snapdragon_x_elite.zip)
+| QNN_CONTEXT_BINARY | float | Snapdragon® 8 Gen 3 Mobile | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-qnn_context_binary-float-qualcomm_snapdragon_8gen3.zip)
+| QNN_CONTEXT_BINARY | float | Qualcomm® QCS8275 (Proxy) | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-qnn_context_binary-float-qualcomm_qcs8275_proxy.zip)
+| QNN_CONTEXT_BINARY | float | Qualcomm® QCS8550 (Proxy) | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-qnn_context_binary-float-qualcomm_qcs8550_proxy.zip)
+| QNN_CONTEXT_BINARY | float | Qualcomm® SA8775P | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-qnn_context_binary-float-qualcomm_sa8775p.zip)
+| QNN_CONTEXT_BINARY | float | Snapdragon® 8 Elite For Galaxy Mobile | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-qnn_context_binary-float-qualcomm_snapdragon_8_elite_for_galaxy.zip)
+| QNN_CONTEXT_BINARY | float | Snapdragon® 8 Elite Gen 5 Mobile | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-qnn_context_binary-float-qualcomm_snapdragon_8_elite_gen5.zip)
+| QNN_CONTEXT_BINARY | float | Qualcomm® SA7255P | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-qnn_context_binary-float-qualcomm_sa7255p.zip)
+| QNN_CONTEXT_BINARY | float | Qualcomm® SA8295P | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-qnn_context_binary-float-qualcomm_sa8295p.zip)
+| QNN_CONTEXT_BINARY | float | Qualcomm® QCS9075 | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-qnn_context_binary-float-qualcomm_qcs9075.zip)
+| QNN_CONTEXT_BINARY | float | Qualcomm® QCS8450 (Proxy) | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/centernet_pose/releases/v0.46.1/centernet_pose-qnn_context_binary-float-qualcomm_qcs8450_proxy.zip)
+
+For more device-specific assets and performance metrics, visit **[CenterNet-Pose on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/centernet_pose)**.
+
+
+### Option 2: Export with Custom Configurations
+
+Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/centernet_pose) 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 [CenterNet-Pose on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/centernet_pose) for usage instructions.
+
+## Model Details
+
+**Model Type:** Model_use_case.pose_estimation
+
+**Model Stats:**
+- Model checkpoint: multi_pose_dla_3x.pth
+- Input resolution: 1 x 3 x 512 x 512
+- Number of parameters: 20.6M
+- Model size: 57.8 MB
+
+## Performance Summary
+| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
+|---|---|---|---|---|---|---
+| CenterNet-Pose | PRECOMPILED_QNN_ONNX | float | Snapdragon® X Elite | 55.388 ms | 44 - 44 MB | NPU
+| CenterNet-Pose | PRECOMPILED_QNN_ONNX | float | Snapdragon® 8 Gen 3 Mobile | 39.636 ms | 3 - 11 MB | NPU
+| CenterNet-Pose | PRECOMPILED_QNN_ONNX | float | Qualcomm® QCS8550 (Proxy) | 54.637 ms | 0 - 48 MB | NPU
+| CenterNet-Pose | PRECOMPILED_QNN_ONNX | float | Qualcomm® QCS9075 | 59.643 ms | 3 - 6 MB | NPU
+| CenterNet-Pose | PRECOMPILED_QNN_ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 31.701 ms | 4 - 10 MB | NPU
+| CenterNet-Pose | PRECOMPILED_QNN_ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 27.761 ms | 3 - 14 MB | NPU
+| CenterNet-Pose | QNN_CONTEXT_BINARY | float | Snapdragon® X Elite | 56.271 ms | 1 - 1 MB | NPU
+| CenterNet-Pose | QNN_CONTEXT_BINARY | float | Snapdragon® 8 Gen 3 Mobile | 38.077 ms | 1 - 8 MB | NPU
+| CenterNet-Pose | QNN_CONTEXT_BINARY | float | Qualcomm® QCS8275 (Proxy) | 101.848 ms | 1 - 10 MB | NPU
+| CenterNet-Pose | QNN_CONTEXT_BINARY | float | Qualcomm® QCS8550 (Proxy) | 54.344 ms | 1 - 3 MB | NPU
+| CenterNet-Pose | QNN_CONTEXT_BINARY | float | Qualcomm® SA8775P | 257.372 ms | 1 - 9 MB | NPU
+| CenterNet-Pose | QNN_CONTEXT_BINARY | float | Qualcomm® QCS9075 | 57.513 ms | 1 - 4 MB | NPU
+| CenterNet-Pose | QNN_CONTEXT_BINARY | float | Qualcomm® QCS8450 (Proxy) | 92.279 ms | 1 - 10 MB | NPU
+| CenterNet-Pose | QNN_CONTEXT_BINARY | float | Qualcomm® SA7255P | 101.848 ms | 1 - 10 MB | NPU
+| CenterNet-Pose | QNN_CONTEXT_BINARY | float | Qualcomm® SA8295P | 77.676 ms | 0 - 5 MB | NPU
+| CenterNet-Pose | QNN_CONTEXT_BINARY | float | Snapdragon® 8 Elite For Galaxy Mobile | 29.527 ms | 1 - 13 MB | NPU
+| CenterNet-Pose | QNN_CONTEXT_BINARY | float | Snapdragon® 8 Elite Gen 5 Mobile | 26.597 ms | 1 - 11 MB | NPU
 
 ## License
 * The license for the original implementation of CenterNet-Pose can be found
   [here](https://github.com/xingyizhou/CenterNet/blob/master/LICENSE).
 
-
-
 ## References
 * [Objects as Points](https://arxiv.org/abs/1904.07850)
 * [Source Model Implementation](https://github.com/xingyizhou/CenterNet)
 
-
-
 ## 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).
-
-

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