v0.46.1

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

PSPNet_float.dlc

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e641c06832d93969cc966a84019b941a14f01fb399edcf35d6e9cb4b08f93899
-size 263161228

PSPNet_float.onnx.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b4e7366490aaf7e95c7e9dcf56526e66fc8a4bce2bd98d2eb97afeac4559096d
-size 245551224

PSPNet_float.tflite

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5230f23ccf1e8362adf33f322c982450bd3dc785a079d086e75a79ea2a5b8d65
-size 262937848

README.md

@@ -10,219 +10,75 @@ pipeline_tag: image-segmentation
 
 ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/web-assets/model_demo.png)
 
-# PSPNet: Optimized for Mobile Deployment
-## Deep learning model for pixel-level semantic segmentation using pyramid pooling
+# PSPNet: Optimized for Qualcomm Devices
 
 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.
 
-This repository provides scripts to run PSPNet on Qualcomm® devices.
-More details on model performance across various devices, can be found
-[here](https://aihub.qualcomm.com/models/pspnet).
+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/pspnet) 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:
 
-### Model Details
+### Option 1: Download Pre-Exported Models
 
-- **Model Type:** Model_use_case.semantic_segmentation
-- **Model Stats:**
-  - Model checkpoint: pspnet101_ade20k.pth
-  - Input resolution: 1x3x473x473
-  - Number of parameters: 65.7M
-  - Model size (float): 251 MB
+Below are pre-exported model assets ready for deployment.
 
-| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
-|---|---|---|---|---|---|---|---|---|
-| PSPNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 1668.832 ms | 117 - 878 MB | NPU | [PSPNet.tflite](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.tflite) |
-| PSPNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 1501.062 ms | 0 - 681 MB | NPU | [PSPNet.dlc](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.dlc) |
-| PSPNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 1517.133 ms | 15 - 533 MB | NPU | [PSPNet.tflite](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.tflite) |
-| PSPNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 1115.893 ms | 3 - 400 MB | NPU | [PSPNet.dlc](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.dlc) |
-| PSPNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 599.337 ms | 128 - 131 MB | NPU | [PSPNet.tflite](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.tflite) |
-| PSPNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 593.125 ms | 3 - 5 MB | NPU | [PSPNet.dlc](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.dlc) |
-| PSPNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 1307.328 ms | 71 - 232 MB | NPU | [PSPNet.onnx.zip](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.onnx.zip) |
-| PSPNet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 3023.934 ms | 119 - 878 MB | NPU | [PSPNet.tflite](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.tflite) |
-| PSPNet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 646.417 ms | 0 - 682 MB | NPU | [PSPNet.dlc](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.dlc) |
-| PSPNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 543.73 ms | 111 - 1069 MB | NPU | [PSPNet.tflite](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.tflite) |
-| PSPNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 473.761 ms | 25 - 921 MB | NPU | [PSPNet.dlc](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.dlc) |
-| PSPNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 997.496 ms | 78 - 801 MB | NPU | [PSPNet.onnx.zip](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.onnx.zip) |
-| PSPNet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 375.278 ms | 110 - 864 MB | NPU | [PSPNet.tflite](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.tflite) |
-| PSPNet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 379.078 ms | 2 - 673 MB | NPU | [PSPNet.dlc](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.dlc) |
-| PSPNet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 975.577 ms | 138 - 716 MB | NPU | [PSPNet.onnx.zip](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.onnx.zip) |
-| PSPNet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 320.19 ms | 67 - 829 MB | NPU | [PSPNet.tflite](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.tflite) |
-| PSPNet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 382.858 ms | 5 - 695 MB | NPU | [PSPNet.dlc](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.dlc) |
-| PSPNet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 1048.343 ms | 13 - 604 MB | NPU | [PSPNet.onnx.zip](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.onnx.zip) |
-| PSPNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 601.791 ms | 3 - 3 MB | NPU | [PSPNet.dlc](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.dlc) |
-| PSPNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1030.685 ms | 265 - 265 MB | NPU | [PSPNet.onnx.zip](https://huggingface.co/qualcomm/PSPNet/blob/main/PSPNet.onnx.zip) |
+| Runtime | Precision | Chipset | SDK Versions | Download |
+|---|---|---|---|---|
+| ONNX | float | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.46.1/pspnet-onnx-float.zip)
+| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.46.1/pspnet-qnn_dlc-float.zip)
+| TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pspnet/releases/v0.46.1/pspnet-tflite-float.zip)
 
+For more device-specific assets and performance metrics, visit **[PSPNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/pspnet)**.
 
 
+### Option 2: Export with Custom Configurations
 
-## Installation
+Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/pspnet) 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.
 
-Install the package via pip:
-```bash
-pip install qai-hub-models
-```
+See our repository for [PSPNet on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/pspnet) for usage instructions.
 
+## Model Details
 
-## Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device
+**Model Type:** Model_use_case.semantic_segmentation
 
-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.pspnet.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.pspnet.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.pspnet.export
-```
-
-
-
-## How does this work?
-
-This [export script](https://aihub.qualcomm.com/models/pspnet/qai_hub_models/models/PSPNet/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.pspnet 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.pspnet.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.pspnet.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 PSPNet's performance across various devices [here](https://aihub.qualcomm.com/models/pspnet).
-Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
+**Model Stats:**
+- Model checkpoint: pspnet101_ade20k.pth
+- Input resolution: 1x3x473x473
+- Number of parameters: 65.7M
+- Model size (float): 251 MB
 
+## Performance Summary
+| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
+|---|---|---|---|---|---|---
+| PSPNet | ONNX | float | Snapdragon® X Elite | 1029.088 ms | 265 - 265 MB | NPU
+| PSPNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 1007.674 ms | 11 - 732 MB | NPU
+| PSPNet | ONNX | float | Qualcomm® QCS8550 (Proxy) | 1313.456 ms | 0 - 160 MB | NPU
+| PSPNet | ONNX | float | Qualcomm® QCS9075 | 1788.258 ms | 8 - 13 MB | NPU
+| PSPNet | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 972.45 ms | 128 - 706 MB | NPU
+| PSPNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 1063.045 ms | 11 - 605 MB | NPU
+| PSPNet | QNN_DLC | float | Snapdragon® X Elite | 531.831 ms | 3 - 3 MB | NPU
+| PSPNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 406.212 ms | 3 - 875 MB | NPU
+| PSPNet | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 1333.575 ms | 0 - 718 MB | NPU
+| PSPNet | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 584.235 ms | 3 - 5 MB | NPU
+| PSPNet | QNN_DLC | float | Qualcomm® QCS9075 | 1751.221 ms | 3 - 135 MB | NPU
+| PSPNet | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 1693.424 ms | 0 - 430 MB | NPU
+| PSPNet | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 295.116 ms | 3 - 719 MB | NPU
+| PSPNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 272.52 ms | 3 - 733 MB | NPU
+| PSPNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 511.644 ms | 127 - 1180 MB | NPU
+| PSPNet | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 1630.453 ms | 126 - 960 MB | NPU
+| PSPNet | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 603.698 ms | 128 - 131 MB | NPU
+| PSPNet | TFLITE | float | Qualcomm® QCS9075 | 1766.35 ms | 0 - 272 MB | NPU
+| PSPNet | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 1561.127 ms | 53 - 628 MB | NPU
+| PSPNet | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 977.818 ms | 116 - 871 MB | NPU
+| PSPNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 323.794 ms | 0 - 836 MB | NPU
 
 ## License
 * The license for the original implementation of PSPNet can be found
@@ -230,9 +86,6 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 
 
 
-
 ## 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).
-
-

tool-versions.yaml

@@ -1,4 +0,0 @@
-tool_versions:
-  onnx:
-    qairt: 2.37.1.250807093845_124904
-    onnx_runtime: 1.23.0