v0.39.0

See https://github.com/quic/ai-hub-models/releases/v0.39.0 for changelog.

.gitattributes

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+DEPLOYMENT_MODEL_LICENSE.pdf filter=lfs diff=lfs merge=lfs -text
+RegNet-Y-800MF_float.dlc filter=lfs diff=lfs merge=lfs -text

DEPLOYMENT_MODEL_LICENSE.pdf

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+oid sha256:4409f93b0e82531303b3e10f52f1fdfb56467a25f05b7441c6bbd8bb8a64b42c
+size 109629

LICENSE

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+The license of the original trained model can be found at https://github.com/pytorch/vision/blob/main/LICENSE.
+The license for the deployable model files (.tflite, .onnx, .dlc, .bin, etc.) can be found in DEPLOYMENT_MODEL_LICENSE.pdf.

README.md

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+---
+library_name: pytorch
+license: other
+tags:
+- android
+pipeline_tag: image-classification
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/regnet_y_800mf/web-assets/model_demo.png)
+
+# RegNet-Y-800MF: Optimized for Mobile Deployment
+## Lightweight convolutional neural network for image classification
+
+RegNet_Y_800MF is part of the RegNet family of models designed for efficient and scalable image classification. It uses a simple yet effective design space to balance performance and computational cost, making it suitable for mobile and edge devices.
+
+This repository provides scripts to run RegNet-Y-800MF on Qualcomm® devices.
+More details on model performance across various devices, can be found
+[here](https://aihub.qualcomm.com/models/regnet_y_800mf).
+
+
+
+### Model Details
+
+- **Model Type:** Model_use_case.image_classification
+- **Model Stats:**
+  - Model checkpoint: regnet_y_800mf-1b27b58c.pth
+  - Input resolution: 1x3x224
+  - Model size: ~6.3 MB
+
+| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
+|---|---|---|---|---|---|---|---|---|
+| RegNet-Y-800MF | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 3.94 ms | 0 - 36 MB | NPU | [RegNet-Y-800MF.tflite](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.tflite) |
+| RegNet-Y-800MF | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 3.809 ms | 1 - 28 MB | NPU | [RegNet-Y-800MF.dlc](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.dlc) |
+| RegNet-Y-800MF | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 1.602 ms | 0 - 50 MB | NPU | [RegNet-Y-800MF.tflite](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.tflite) |
+| RegNet-Y-800MF | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 2.047 ms | 0 - 41 MB | NPU | [RegNet-Y-800MF.dlc](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.dlc) |
+| RegNet-Y-800MF | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 1.329 ms | 0 - 86 MB | NPU | [RegNet-Y-800MF.tflite](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.tflite) |
+| RegNet-Y-800MF | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 1.305 ms | 1 - 14 MB | NPU | [RegNet-Y-800MF.dlc](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.dlc) |
+| RegNet-Y-800MF | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 1.329 ms | 0 - 46 MB | NPU | [RegNet-Y-800MF.onnx.zip](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.onnx.zip) |
+| RegNet-Y-800MF | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 1.879 ms | 0 - 36 MB | NPU | [RegNet-Y-800MF.tflite](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.tflite) |
+| RegNet-Y-800MF | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 1.82 ms | 1 - 28 MB | NPU | [RegNet-Y-800MF.dlc](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.dlc) |
+| RegNet-Y-800MF | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 3.94 ms | 0 - 36 MB | NPU | [RegNet-Y-800MF.tflite](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.tflite) |
+| RegNet-Y-800MF | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 3.809 ms | 1 - 28 MB | NPU | [RegNet-Y-800MF.dlc](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.dlc) |
+| RegNet-Y-800MF | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 1.329 ms | 0 - 86 MB | NPU | [RegNet-Y-800MF.tflite](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.tflite) |
+| RegNet-Y-800MF | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 1.306 ms | 1 - 11 MB | NPU | [RegNet-Y-800MF.dlc](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.dlc) |
+| RegNet-Y-800MF | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 2.089 ms | 0 - 39 MB | NPU | [RegNet-Y-800MF.tflite](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.tflite) |
+| RegNet-Y-800MF | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 2.036 ms | 1 - 33 MB | NPU | [RegNet-Y-800MF.dlc](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.dlc) |
+| RegNet-Y-800MF | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 1.328 ms | 0 - 85 MB | NPU | [RegNet-Y-800MF.tflite](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.tflite) |
+| RegNet-Y-800MF | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 1.305 ms | 1 - 12 MB | NPU | [RegNet-Y-800MF.dlc](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.dlc) |
+| RegNet-Y-800MF | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 1.879 ms | 0 - 36 MB | NPU | [RegNet-Y-800MF.tflite](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.tflite) |
+| RegNet-Y-800MF | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 1.82 ms | 1 - 28 MB | NPU | [RegNet-Y-800MF.dlc](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.dlc) |
+| RegNet-Y-800MF | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 0.865 ms | 0 - 52 MB | NPU | [RegNet-Y-800MF.tflite](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.tflite) |
+| RegNet-Y-800MF | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 0.853 ms | 0 - 41 MB | NPU | [RegNet-Y-800MF.dlc](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.dlc) |
+| RegNet-Y-800MF | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 0.846 ms | 0 - 48 MB | NPU | [RegNet-Y-800MF.onnx.zip](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.onnx.zip) |
+| RegNet-Y-800MF | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 0.691 ms | 0 - 41 MB | NPU | [RegNet-Y-800MF.tflite](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.tflite) |
+| RegNet-Y-800MF | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 0.663 ms | 1 - 35 MB | NPU | [RegNet-Y-800MF.dlc](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.dlc) |
+| RegNet-Y-800MF | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 0.689 ms | 0 - 36 MB | NPU | [RegNet-Y-800MF.onnx.zip](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.onnx.zip) |
+| RegNet-Y-800MF | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | TFLITE | 0.596 ms | 0 - 39 MB | NPU | [RegNet-Y-800MF.tflite](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.tflite) |
+| RegNet-Y-800MF | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_DLC | 0.62 ms | 0 - 35 MB | NPU | [RegNet-Y-800MF.dlc](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.dlc) |
+| RegNet-Y-800MF | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | ONNX | 0.71 ms | 0 - 35 MB | NPU | [RegNet-Y-800MF.onnx.zip](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.onnx.zip) |
+| RegNet-Y-800MF | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 1.53 ms | 48 - 48 MB | NPU | [RegNet-Y-800MF.dlc](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.dlc) |
+| RegNet-Y-800MF | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 1.261 ms | 14 - 14 MB | NPU | [RegNet-Y-800MF.onnx.zip](https://huggingface.co/qualcomm/RegNet-Y-800MF/blob/main/RegNet-Y-800MF.onnx.zip) |
+
+
+
+
+## Installation
+
+
+Install the package via pip:
+```bash
+pip install qai-hub-models
+```
+
+
+## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
+
+Sign-in to [Qualcomm® AI Hub](https://app.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://app.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.regnet_y_800mf.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.regnet_y_800mf.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.regnet_y_800mf.export
+```
+
+
+
+## How does this work?
+
+This [export script](https://aihub.qualcomm.com/models/regnet_y_800mf/qai_hub_models/models/RegNet-Y-800MF/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.regnet_y_800mf 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. [Sign up for access](https://myaccount.qualcomm.com/signup).
+
+
+
+
+## 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 RegNet-Y-800MF's performance across various devices [here](https://aihub.qualcomm.com/models/regnet_y_800mf).
+Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
+
+
+## License
+* The license for the original implementation of RegNet-Y-800MF can be found
+  [here](https://github.com/pytorch/vision/blob/main/LICENSE).
+* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)
+
+
+
+
+## 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).
+
+

RegNet-Y-800MF_float.dlc

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RegNet-Y-800MF_float.tflite

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tool-versions.yaml

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+tool_versions:
+  onnx:
+    qairt: 2.37.1.250807093845_124904
+    onnx_runtime: 1.22.2