v0.41.2

See https://github.com/quic/ai-hub-models/releases/v0.41.2 for changelog.

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+DEPLOYMENT_MODEL_LICENSE.pdf filter=lfs diff=lfs merge=lfs -text
+EyeGaze_float.dlc filter=lfs diff=lfs merge=lfs -text

DEPLOYMENT_MODEL_LICENSE.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4409f93b0e82531303b3e10f52f1fdfb56467a25f05b7441c6bbd8bb8a64b42c
+size 109629

EyeGaze_float.dlc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:81665b8fa17ebd9daa40ae43ab8f51dc23139351e2c9268b48d949db6742b17f
+size 10496460

EyeGaze_float.onnx.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:99c35c1452efdd36f16357a17b513b0e33c9a0bfec02f8d008f4007d7834ed60
+size 9295227

EyeGaze_float.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39672da8dd555f08ed2ce384898f6639c48c8ee270671f5410896dc8edea3cf1
+size 10079496

EyeGaze_w8a16.onnx.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:633ef17406fb537789439e8f284d99175751333d73a220e4766a64830607555b
+size 3516939

LICENSE

@@ -0,0 +1,2 @@
+The license of the original trained model can be found at https://github.com/quic/ai-hub-models/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

@@ -0,0 +1,265 @@
+---
+library_name: pytorch
+license: other
+tags:
+- real_time
+- android
+pipeline_tag: gaze-estimation
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/eyegaze/web-assets/model_demo.png)
+
+# EyeGaze: Optimized for Mobile Deployment
+## Eye gaze estimation from cropped eye images
+
+
+Predicts gaze direction (pitch, yaw) from 96x160 grayscale eye images using the EyeNet model.
+
+This model is an implementation of EyeGaze found [here](https://github.com/david-wb/gaze-estimation).
+
+
+This repository provides scripts to run EyeGaze on Qualcomm® devices.
+More details on model performance across various devices, can be found
+[here](https://aihub.qualcomm.com/models/eyegaze).
+
+
+
+### Model Details
+
+- **Model Type:** Model_use_case.gaze_estimation
+- **Model Stats:**
+  - Model checkpoint: checkpoint.pt
+  - Input resolution: 96x160
+  - Number of parameters: 2.58M
+  - Model size (float): 9.6MB
+  - Model size (w8a16): 3.3 MB
+
+| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
+|---|---|---|---|---|---|---|---|---|
+| EyeGaze | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 30.665 ms | 3 - 13 MB | CPU | [EyeGaze.tflite](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.tflite) |
+| EyeGaze | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 82.703 ms | 38 - 50 MB | CPU | [EyeGaze.dlc](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.dlc) |
+| EyeGaze | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 21.118 ms | 3 - 20 MB | CPU | [EyeGaze.tflite](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.tflite) |
+| EyeGaze | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 67.891 ms | 38 - 63 MB | CPU | [EyeGaze.dlc](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.dlc) |
+| EyeGaze | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 18.86 ms | 0 - 3 MB | CPU | [EyeGaze.tflite](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.tflite) |
+| EyeGaze | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 46.184 ms | 38 - 43 MB | CPU | [EyeGaze.dlc](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.dlc) |
+| EyeGaze | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 25.739 ms | 49 - 52 MB | CPU | [EyeGaze.onnx.zip](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.onnx.zip) |
+| EyeGaze | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 26.898 ms | 3 - 12 MB | CPU | [EyeGaze.tflite](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.tflite) |
+| EyeGaze | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 51.261 ms | 38 - 50 MB | CPU | [EyeGaze.dlc](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.dlc) |
+| EyeGaze | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 30.665 ms | 3 - 13 MB | CPU | [EyeGaze.tflite](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.tflite) |
+| EyeGaze | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 82.703 ms | 38 - 50 MB | CPU | [EyeGaze.dlc](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.dlc) |
+| EyeGaze | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 18.795 ms | 3 - 5 MB | CPU | [EyeGaze.tflite](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.tflite) |
+| EyeGaze | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 46.904 ms | 37 - 42 MB | CPU | [EyeGaze.dlc](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.dlc) |
+| EyeGaze | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 17.265 ms | 3 - 19 MB | CPU | [EyeGaze.tflite](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.tflite) |
+| EyeGaze | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 44.87 ms | 38 - 59 MB | CPU | [EyeGaze.dlc](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.dlc) |
+| EyeGaze | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 18.952 ms | 3 - 6 MB | CPU | [EyeGaze.tflite](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.tflite) |
+| EyeGaze | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 49.783 ms | 36 - 41 MB | CPU | [EyeGaze.dlc](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.dlc) |
+| EyeGaze | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 26.898 ms | 3 - 12 MB | CPU | [EyeGaze.tflite](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.tflite) |
+| EyeGaze | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 51.261 ms | 38 - 50 MB | CPU | [EyeGaze.dlc](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.dlc) |
+| EyeGaze | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 16.794 ms | 3 - 26 MB | CPU | [EyeGaze.tflite](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.tflite) |
+| EyeGaze | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 34.825 ms | 38 - 61 MB | CPU | [EyeGaze.dlc](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.dlc) |
+| EyeGaze | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 22.375 ms | 50 - 70 MB | CPU | [EyeGaze.onnx.zip](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.onnx.zip) |
+| EyeGaze | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 13.752 ms | 3 - 15 MB | CPU | [EyeGaze.tflite](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.tflite) |
+| EyeGaze | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 40.366 ms | 38 - 53 MB | CPU | [EyeGaze.dlc](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.dlc) |
+| EyeGaze | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 21.12 ms | 50 - 66 MB | CPU | [EyeGaze.onnx.zip](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.onnx.zip) |
+| EyeGaze | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | TFLITE | 12.532 ms | 3 - 13 MB | CPU | [EyeGaze.tflite](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.tflite) |
+| EyeGaze | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_DLC | 25.006 ms | 37 - 51 MB | CPU | [EyeGaze.dlc](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.dlc) |
+| EyeGaze | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | ONNX | 20.456 ms | 50 - 62 MB | CPU | [EyeGaze.onnx.zip](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.onnx.zip) |
+| EyeGaze | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 65.476 ms | 13 - 13 MB | CPU | [EyeGaze.dlc](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.dlc) |
+| EyeGaze | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 10.115 ms | 67 - 67 MB | CPU | [EyeGaze.onnx.zip](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze.onnx.zip) |
+| EyeGaze | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 43.815 ms | 69 - 73 MB | CPU | [EyeGaze.onnx.zip](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze_w8a16.onnx.zip) |
+| EyeGaze | w8a16 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | ONNX | 66.667 ms | 69 - 83 MB | CPU | [EyeGaze.onnx.zip](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze_w8a16.onnx.zip) |
+| EyeGaze | w8a16 | RB5 (Proxy) | Qualcomm® QCS8250 (Proxy) | ONNX | 55.317 ms | 63 - 76 MB | CPU | [EyeGaze.onnx.zip](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze_w8a16.onnx.zip) |
+| EyeGaze | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 31.679 ms | 71 - 100 MB | CPU | [EyeGaze.onnx.zip](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze_w8a16.onnx.zip) |
+| EyeGaze | w8a16 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 27.328 ms | 71 - 86 MB | CPU | [EyeGaze.onnx.zip](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze_w8a16.onnx.zip) |
+| EyeGaze | w8a16 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | ONNX | 27.052 ms | 68 - 83 MB | CPU | [EyeGaze.onnx.zip](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze_w8a16.onnx.zip) |
+| EyeGaze | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 17.767 ms | 100 - 100 MB | CPU | [EyeGaze.onnx.zip](https://huggingface.co/qualcomm/EyeGaze/blob/main/EyeGaze_w8a16.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.eyegaze.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.eyegaze.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.eyegaze.export
+```
+
+
+
+## How does this work?
+
+This [export script](https://aihub.qualcomm.com/models/eyegaze/qai_hub_models/models/EyeGaze/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.eyegaze 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).
+
+
+
+## Run demo on a cloud-hosted device
+
+You can also run the demo on-device.
+
+```bash
+python -m qai_hub_models.models.eyegaze.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.eyegaze.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 EyeGaze's performance across various devices [here](https://aihub.qualcomm.com/models/eyegaze).
+Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
+
+
+## License
+* The license for the original implementation of EyeGaze can be found
+  [here](https://github.com/quic/ai-hub-models/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)
+
+
+
+## References
+* [Source Model Implementation](https://github.com/david-wb/gaze-estimation)
+
+
+
+## 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

@@ -0,0 +1,3 @@
+tool_versions:
+  onnx:
+    onnx_runtime: 1.23.0