v0.51.0

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See https://github.com/qualcomm/ai-hub-models/releases/v0.51.0 for changelog.

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README.md +98 -214
SwinV2-Base_float.onnx.zip +0 -3
SwinV2-Base_float.tflite +0 -3
release_assets.json +46 -0
tool-versions.yaml +0 -4

README.md CHANGED Viewed

@@ -11,232 +11,116 @@ pipeline_tag: image-classification
 ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/web-assets/model_demo.png)
-# SwinV2-Base: Optimized for Mobile Deployment
-## Imagenet classifier and general purpose backbone
 SwinV2Base is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
-This model is an implementation of SwinV2-Base found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py).
-This repository provides scripts to run SwinV2-Base on Qualcomm® devices.
-More details on model performance across various devices, can be found
-[here](https://aihub.qualcomm.com/models/swinv2_base).
-### Model Details
-- **Model Type:** Model_use_case.image_classification
-- **Model Stats:**
-  - Model checkpoint: Imagenet
-  - Input resolution: 256x256
-  - Number of parameters: 88.8M
-  - Model size (float): 339 MB
-  - Model size (w8a16): 90.2 MB
-| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
-|---|---|---|---|---|---|---|---|---|
-| SwinV2-Base | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 81.861 ms | 0 - 614 MB | NPU | [SwinV2-Base.tflite](https://huggingface.co/qualcomm/SwinV2-Base/blob/main/SwinV2-Base.tflite) |
-| SwinV2-Base | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 46.905 ms | 0 - 730 MB | NPU | [SwinV2-Base.tflite](https://huggingface.co/qualcomm/SwinV2-Base/blob/main/SwinV2-Base.tflite) |
-| SwinV2-Base | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 35.029 ms | 0 - 4 MB | NPU | [SwinV2-Base.tflite](https://huggingface.co/qualcomm/SwinV2-Base/blob/main/SwinV2-Base.tflite) |
-| SwinV2-Base | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 38.721 ms | 0 - 596 MB | NPU | [SwinV2-Base.tflite](https://huggingface.co/qualcomm/SwinV2-Base/blob/main/SwinV2-Base.tflite) |
-| SwinV2-Base | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 81.861 ms | 0 - 614 MB | NPU | [SwinV2-Base.tflite](https://huggingface.co/qualcomm/SwinV2-Base/blob/main/SwinV2-Base.tflite) |
-| SwinV2-Base | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 47.037 ms | 0 - 581 MB | NPU | [SwinV2-Base.tflite](https://huggingface.co/qualcomm/SwinV2-Base/blob/main/SwinV2-Base.tflite) |
-| SwinV2-Base | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 38.721 ms | 0 - 596 MB | NPU | [SwinV2-Base.tflite](https://huggingface.co/qualcomm/SwinV2-Base/blob/main/SwinV2-Base.tflite) |
-| SwinV2-Base | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 24.258 ms | 0 - 745 MB | NPU | [SwinV2-Base.tflite](https://huggingface.co/qualcomm/SwinV2-Base/blob/main/SwinV2-Base.tflite) |
-| SwinV2-Base | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 17.996 ms | 0 - 600 MB | NPU | [SwinV2-Base.tflite](https://huggingface.co/qualcomm/SwinV2-Base/blob/main/SwinV2-Base.tflite) |
-| SwinV2-Base | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 35.181 ms | 30 - 290 MB | NPU | [SwinV2-Base.onnx.zip](https://huggingface.co/qualcomm/SwinV2-Base/blob/main/SwinV2-Base.onnx.zip) |
-| SwinV2-Base | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 14.619 ms | 0 - 653 MB | NPU | [SwinV2-Base.tflite](https://huggingface.co/qualcomm/SwinV2-Base/blob/main/SwinV2-Base.tflite) |
-| SwinV2-Base | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 40.283 ms | 29 - 318 MB | NPU | [SwinV2-Base.onnx.zip](https://huggingface.co/qualcomm/SwinV2-Base/blob/main/SwinV2-Base.onnx.zip) |
-| SwinV2-Base | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 64.884 ms | 178 - 178 MB | NPU | [SwinV2-Base.onnx.zip](https://huggingface.co/qualcomm/SwinV2-Base/blob/main/SwinV2-Base.onnx.zip) |
-## 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.swinv2_base.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.swinv2_base.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.swinv2_base.export
-```
-## How does this work?
-This [export script](https://aihub.qualcomm.com/models/swinv2_base/qai_hub_models/models/SwinV2-Base/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.swinv2_base 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.swinv2_base.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.swinv2_base.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 SwinV2-Base's performance across various devices [here](https://aihub.qualcomm.com/models/swinv2_base).
-Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 * The license for the original implementation of SwinV2-Base can be found
   [here](https://github.com/pytorch/vision/blob/main/LICENSE).
 ## References
 * [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883)
 * [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py)
 ## 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).

 ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/web-assets/model_demo.png)
+# SwinV2-Base: Optimized for Qualcomm Devices
 SwinV2Base is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
+This is based on the implementation of SwinV2-Base found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py).
+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/main/src/qai_hub_models/models/swinv2_base) 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 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.51.0/swinv2_base-onnx-float.zip)
+| ONNX | w8a16 | Universal | QAIRT 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.51.0/swinv2_base-onnx-w8a16.zip)
+| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.51.0/swinv2_base-qnn_dlc-float.zip)
+| QNN_DLC | w8a16 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.51.0/swinv2_base-qnn_dlc-w8a16.zip)
+| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.51.0/swinv2_base-tflite-float.zip)
+For more device-specific assets and performance metrics, visit **[SwinV2-Base on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/swinv2_base)**.
+### Option 2: Export with Custom Configurations
+Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/swinv2_base) 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 [SwinV2-Base on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/swinv2_base) for usage instructions.
+## Model Details
+**Model Type:** Model_use_case.image_classification
+**Model Stats:**
+- Model checkpoint: Imagenet
+- Input resolution: 256x256
+- Number of parameters: 88.8M
+- Model size (float): 339 MB
+- Model size (w8a16): 90.2 MB
+## Performance Summary
+| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
+|---|---|---|---|---|---|---
+| SwinV2-Base | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 11.733 ms | 1 - 1020 MB | NPU
+| SwinV2-Base | ONNX | float | Snapdragon® X2 Elite | 12.544 ms | 179 - 179 MB | NPU
+| SwinV2-Base | ONNX | float | Snapdragon® X Elite | 32.734 ms | 178 - 178 MB | NPU
+| SwinV2-Base | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 21.746 ms | 0 - 2329 MB | NPU
+| SwinV2-Base | ONNX | float | Qualcomm® QCS8550 (Proxy) | 31.491 ms | 0 - 196 MB | NPU
+| SwinV2-Base | ONNX | float | Qualcomm® QCS9075 | 38.785 ms | 0 - 4 MB | NPU
+| SwinV2-Base | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 16.303 ms | 0 - 1012 MB | NPU
+| SwinV2-Base | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 11.395 ms | 0 - 1005 MB | NPU
+| SwinV2-Base | ONNX | w8a16 | Snapdragon® X2 Elite | 12.122 ms | 97 - 97 MB | NPU
+| SwinV2-Base | ONNX | w8a16 | Snapdragon® X Elite | 31.018 ms | 97 - 97 MB | NPU
+| SwinV2-Base | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 20.319 ms | 0 - 1295 MB | NPU
+| SwinV2-Base | ONNX | w8a16 | Qualcomm® QCS6490 | 1306.327 ms | 62 - 91 MB | CPU
+| SwinV2-Base | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 29.734 ms | 0 - 102 MB | NPU
+| SwinV2-Base | ONNX | w8a16 | Qualcomm® QCS9075 | 34.846 ms | 0 - 3 MB | NPU
+| SwinV2-Base | ONNX | w8a16 | Qualcomm® QCM6690 | 713.751 ms | 80 - 101 MB | CPU
+| SwinV2-Base | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 14.665 ms | 0 - 866 MB | NPU
+| SwinV2-Base | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 662.899 ms | 156 - 178 MB | CPU
+| SwinV2-Base | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 11.557 ms | 0 - 424 MB | NPU
+| SwinV2-Base | QNN_DLC | float | Snapdragon® X2 Elite | 12.375 ms | 1 - 1 MB | NPU
+| SwinV2-Base | QNN_DLC | float | Snapdragon® X Elite | 29.74 ms | 1 - 1 MB | NPU
+| SwinV2-Base | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 19.935 ms | 1 - 552 MB | NPU
+| SwinV2-Base | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 74.006 ms | 1 - 393 MB | NPU
+| SwinV2-Base | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 28.682 ms | 1 - 3 MB | NPU
+| SwinV2-Base | QNN_DLC | float | Qualcomm® SA8775P | 31.978 ms | 1 - 392 MB | NPU
+| SwinV2-Base | QNN_DLC | float | Qualcomm® QCS9075 | 37.916 ms | 1 - 3 MB | NPU
+| SwinV2-Base | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 44.29 ms | 0 - 538 MB | NPU
+| SwinV2-Base | QNN_DLC | float | Qualcomm® SA7255P | 74.006 ms | 1 - 393 MB | NPU
+| SwinV2-Base | QNN_DLC | float | Qualcomm® SA8295P | 38.42 ms | 1 - 384 MB | NPU
+| SwinV2-Base | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 15.045 ms | 1 - 398 MB | NPU
+| SwinV2-Base | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 11.362 ms | 0 - 943 MB | NPU
+| SwinV2-Base | QNN_DLC | w8a16 | Snapdragon® X2 Elite | 12.461 ms | 0 - 0 MB | NPU
+| SwinV2-Base | QNN_DLC | w8a16 | Snapdragon® X Elite | 31.327 ms | 0 - 0 MB | NPU
+| SwinV2-Base | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 20.03 ms | 0 - 621 MB | NPU
+| SwinV2-Base | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 53.241 ms | 0 - 934 MB | NPU
+| SwinV2-Base | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 29.722 ms | 0 - 3 MB | NPU
+| SwinV2-Base | QNN_DLC | w8a16 | Qualcomm® SA8775P | 29.923 ms | 0 - 916 MB | NPU
+| SwinV2-Base | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 36.121 ms | 0 - 2 MB | NPU
+| SwinV2-Base | QNN_DLC | w8a16 | Qualcomm® SA7255P | 53.241 ms | 0 - 934 MB | NPU
+| SwinV2-Base | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 15.127 ms | 0 - 888 MB | NPU
+| SwinV2-Base | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 11.961 ms | 0 - 969 MB | NPU
+| SwinV2-Base | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 21.923 ms | 0 - 655 MB | NPU
+| SwinV2-Base | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 75.284 ms | 0 - 902 MB | NPU
+| SwinV2-Base | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 32.027 ms | 0 - 3 MB | NPU
+| SwinV2-Base | TFLITE | float | Qualcomm® SA8775P | 35.296 ms | 0 - 847 MB | NPU
+| SwinV2-Base | TFLITE | float | Qualcomm® QCS9075 | 39.731 ms | 0 - 180 MB | NPU
+| SwinV2-Base | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 46.679 ms | 0 - 647 MB | NPU
+| SwinV2-Base | TFLITE | float | Qualcomm® SA7255P | 75.284 ms | 0 - 902 MB | NPU
+| SwinV2-Base | TFLITE | float | Qualcomm® SA8295P | 43.86 ms | 0 - 474 MB | NPU
+| SwinV2-Base | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 15.94 ms | 0 - 904 MB | NPU
 ## License
 * The license for the original implementation of SwinV2-Base can be found
   [here](https://github.com/pytorch/vision/blob/main/LICENSE).
 ## References
 * [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883)
 * [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py)
 ## 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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+{
+    "version": "0.51.0",
+    "precisions": {
+        "float": {
+            "universal_assets": {
+                "tflite": {
+                    "tool_versions": {
+                        "qairt": "2.45.0.260326154327",
+                        "litert": "1.4.2"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.51.0/swinv2_base-tflite-float.zip"
+                },
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+                    "tool_versions": {
+                        "qairt": "2.45.0.260326154327"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.51.0/swinv2_base-qnn_dlc-float.zip"
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+                }
+            }
+        },
+        "w8a16": {
+            "universal_assets": {
+                "qnn_dlc": {
+                    "tool_versions": {
+                        "qairt": "2.45.0.260326154327"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.51.0/swinv2_base-qnn_dlc-w8a16.zip"
+                },
+                "onnx": {
+                    "tool_versions": {
+                        "qairt": "2.42.0.251225135753_193295",
+                        "onnx_runtime": "1.24.3"
+                    },
+                    "download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/swinv2_base/releases/v0.51.0/swinv2_base-onnx-w8a16.zip"
+                }
+            }
+        }
+    }
+}

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