Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,224 @@
+---
+library_name: pytorch
+license: mit
+pipeline_tag: image-to-text
+tags:
+- android
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/trocr/web-assets/banner.png)
+
+# TrOCR: Optimized for Mobile Deployment
+## Transformer based model for state-of-the-art optical character recognition (OCR) on both printed and handwritten text
+
+End-to-end text recognition approach with pre-trained image transformer and text transformer models for both image understanding and wordpiece-level text generation.
+
+This model is an implementation of TrOCR found [here](https://huggingface.co/microsoft/trocr-small-stage1).
+This repository provides scripts to run TrOCR on Qualcomm® devices.
+More details on model performance across various devices, can be found
+[here](https://aihub.qualcomm.com/models/trocr).
+
+
+### Model Details
+
+- **Model Type:** Image to text
+- **Model Stats:**
+  - Model checkpoint: trocr-small-stage1
+  - Input resolution: 320x320
+  - Number of parameters (TrOCREncoder): 23.0M
+  - Model size (TrOCREncoder): 87.8 MB
+  - Number of parameters (TrOCRDecoder): 38.3M
+  - Model size (TrOCRDecoder): 146 MB
+
+
+| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
+| ---|---|---|---|---|---|---|---|
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 244.369 ms | 7 - 10 MB | FP16 | NPU |  [TrOCREncoder.tflite](https://huggingface.co/qualcomm/TrOCR/blob/main/TrOCREncoder.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 2.82 ms | 0 - 2 MB | FP16 | NPU |  [TrOCRDecoder.tflite](https://huggingface.co/qualcomm/TrOCR/blob/main/TrOCRDecoder.tflite) 
+
+
+## Installation
+
+This model can be installed as a Python package via pip.
+
+```bash
+pip install "qai-hub-models[trocr]"
+```
+
+
+
+## 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.trocr.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.trocr.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.trocr.export
+```
+
+```
+Profile Job summary of TrOCREncoder
+--------------------------------------------------
+Device: Samsung Galaxy S23 Ultra (13)
+Estimated Inference Time: 244.37 ms
+Estimated Peak Memory Range: 6.96-9.97 MB
+Compute Units: NPU (627) | Total (627)
+
+Profile Job summary of TrOCRDecoder
+--------------------------------------------------
+Device: Samsung Galaxy S23 Ultra (13)
+Estimated Inference Time: 2.82 ms
+Estimated Peak Memory Range: 0.02-2.11 MB
+Compute Units: NPU (394) | Total (394)
+
+
+```
+## How does this work?
+
+This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/TrOCR/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.trocr import Model
+
+# Load the model
+torch_model = Model.from_pretrained()
+torch_model.eval()
+
+# Device
+device = hub.Device("Samsung Galaxy S23")
+
+# 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 early access](https://aihub.qualcomm.com/sign-up).
+
+
+
+## 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 TrOCR's performance across various devices [here](https://aihub.qualcomm.com/models/trocr).
+Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
+
+## License
+- The license for the original implementation of TrOCR can be found
+  [here](https://github.com/microsoft/unilm/blob/master/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
+* [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282)
+* [Source Model Implementation](https://huggingface.co/microsoft/trocr-small-stage1)
+
+## Community
+* Join [our AI Hub Slack community](https://join.slack.com/t/qualcomm-ai-hub/shared_invite/zt-2dgf95loi-CXHTDRR1rvPgQWPO~ZZZJg) to collaborate, post questions and learn more about on-device AI.
+* For questions or feedback please [reach out to us](mailto:[email protected]).
+
+