v0.46.1
Browse filesSee https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.
- EasyOCR_EasyOCRDetector_float.dlc +0 -3
- EasyOCR_EasyOCRDetector_float.onnx.zip +0 -3
- EasyOCR_EasyOCRDetector_float.tflite +0 -3
- EasyOCR_EasyOCRDetector_w8a8.tflite +0 -3
- EasyOCR_EasyOCRRecognizer_float.dlc +0 -3
- EasyOCR_EasyOCRRecognizer_float.onnx.zip +0 -3
- EasyOCR_EasyOCRRecognizer_float.tflite +0 -3
- EasyOCR_EasyOCRRecognizer_w8a8.tflite +0 -3
- README.md +127 -267
- tool-versions.yaml +0 -3
EasyOCR_EasyOCRDetector_float.dlc
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EasyOCR_EasyOCRRecognizer_w8a8.tflite
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README.md
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# EasyOCR: Optimized for
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## Ready-to-use OCR with 80+ supported languages and all popular writing scripts
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EasyOCR is a machine learning model that can recognize text in images. It supports 80+ supported languages and all popular writing scripts.
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| EasyOCRDetector |
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| EasyOCRRecognizer |
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Qualcomm®
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## Demo off target
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The package contains a simple end-to-end demo that downloads pre-trained
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weights and runs this model on a sample input.
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```bash
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python -m qai_hub_models.models.easyocr.demo
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```
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The above demo runs a reference implementation of pre-processing, model
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inference, and post processing.
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
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environment, please add the following to your cell (instead of the above).
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```
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%run -m qai_hub_models.models.easyocr.demo
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```
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### Run model on a cloud-hosted device
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In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
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device. This script does the following:
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* Performance check on-device on a cloud-hosted device
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* Downloads compiled assets that can be deployed on-device for Android.
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* Accuracy check between PyTorch and on-device outputs.
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```bash
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python -m qai_hub_models.models.easyocr.export
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```
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## How does this work?
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This [export script](https://aihub.qualcomm.com/models/easyocr/qai_hub_models/models/EasyOCR/export.py)
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leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
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on-device. Lets go through each step below in detail:
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Step 1: **Compile model for on-device deployment**
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To compile a PyTorch model for on-device deployment, we first trace the model
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in memory using the `jit.trace` and then call the `submit_compile_job` API.
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```python
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import torch
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import qai_hub as hub
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from qai_hub_models.models.easyocr import Model
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# Load the model
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torch_model = Model.from_pretrained()
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# Device
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device = hub.Device("Samsung Galaxy S25")
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# Trace model
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input_shape = torch_model.get_input_spec()
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sample_inputs = torch_model.sample_inputs()
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pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
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# Compile model on a specific device
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compile_job = hub.submit_compile_job(
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model=pt_model,
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device=device,
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input_specs=torch_model.get_input_spec(),
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)
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# Get target model to run on-device
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target_model = compile_job.get_target_model()
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```
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Step 2: **Performance profiling on cloud-hosted device**
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`target_model`. Note that this scripts runs the model on a device automatically
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provisioned in the cloud. Once the job is submitted, you can navigate to a
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provided job URL to view a variety of on-device performance metrics.
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```python
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)
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```
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Step 3: **Verify on-device accuracy**
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To verify the accuracy of the model on-device, you can run on-device inference
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on sample input data on the same cloud hosted device.
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```python
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input_data = torch_model.sample_inputs()
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inference_job = hub.submit_inference_job(
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model=target_model,
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device=device,
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inputs=input_data,
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)
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on_device_output = inference_job.download_output_data()
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```
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With the output of the model, you can compute like PSNR, relative errors or
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spot check the output with expected output.
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**Note**: This on-device profiling and inference requires access to Qualcomm®
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AI Hub Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
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## Deploying compiled model to Android
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The models can be deployed using multiple runtimes:
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- TensorFlow Lite (`.tflite` export): [This
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tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
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guide to deploy the .tflite model in an Android application.
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- QNN (`.so` export ): This [sample
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app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
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provides instructions on how to use the `.so` shared library in an Android application.
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## View on Qualcomm® AI Hub
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Get more details on EasyOCR's performance across various devices [here](https://aihub.qualcomm.com/models/easyocr).
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Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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## License
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* The license for the original implementation of EasyOCR can be found
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[here](https://github.com/JaidedAI/EasyOCR/blob/master/LICENSE).
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## References
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* [Source Model Implementation](https://github.com/JaidedAI/EasyOCR)
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## Community
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* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
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* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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# EasyOCR: Optimized for Qualcomm Devices
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EasyOCR is a machine learning model that can recognize text in images. It supports 80+ supported languages and all popular writing scripts.
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This is based on the implementation of EasyOCR found [here](https://github.com/JaidedAI/EasyOCR).
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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/easyocr) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
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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.
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## Getting Started
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There are two ways to deploy this model on your device:
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### Option 1: Download Pre-Exported Models
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Below are pre-exported model assets ready for deployment.
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| Runtime | Precision | Chipset | SDK Versions | Download |
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|---|---|---|---|---|
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| 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/easyocr/releases/v0.46.1/easyocr-onnx-float.zip)
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| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/easyocr/releases/v0.46.1/easyocr-qnn_dlc-float.zip)
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| 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/easyocr/releases/v0.46.1/easyocr-tflite-float.zip)
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| TFLITE | w8a8 | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/easyocr/releases/v0.46.1/easyocr-tflite-w8a8.zip)
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For more device-specific assets and performance metrics, visit **[EasyOCR on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/easyocr)**.
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### Option 2: Export with Custom Configurations
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Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/easyocr) Python library to compile and export the model with your own:
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- Custom weights (e.g., fine-tuned checkpoints)
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- Custom input shapes
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- Target device and runtime configurations
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This option is ideal if you need to customize the model beyond the default configuration provided here.
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See our repository for [EasyOCR on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/easyocr) for usage instructions.
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## Model Details
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**Model Type:** Model_use_case.image_to_text
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**Model Stats:**
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- Model checkpoint: easyocr-small-stage1
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- Input resolution: 608x800
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- Number of parameters (EasyOCRDetector): 20.8M
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- Model size (EasyOCRDetector) (float): 79.2 MB
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- Number of parameters (EasyOCRRecognizer): 3.84M
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- Model size (EasyOCRRecognizer) (float): 14.7 MB
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## Performance Summary
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| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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|---|---|---|---|---|---|---
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| 64 |
+
| EasyOCRDetector | ONNX | float | Snapdragon® X Elite | 38.44 ms | 35 - 35 MB | NPU
|
| 65 |
+
| EasyOCRDetector | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 27.117 ms | 2 - 189 MB | NPU
|
| 66 |
+
| EasyOCRDetector | ONNX | float | Qualcomm® QCS8550 (Proxy) | 37.395 ms | 0 - 44 MB | NPU
|
| 67 |
+
| EasyOCRDetector | ONNX | float | Qualcomm® QCS9075 | 70.247 ms | 5 - 14 MB | NPU
|
| 68 |
+
| EasyOCRDetector | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 21.805 ms | 3 - 120 MB | NPU
|
| 69 |
+
| EasyOCRDetector | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 16.439 ms | 7 - 127 MB | NPU
|
| 70 |
+
| EasyOCRDetector | QNN_DLC | float | Snapdragon® X Elite | 41.089 ms | 6 - 6 MB | NPU
|
| 71 |
+
| EasyOCRDetector | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 28.997 ms | 6 - 240 MB | NPU
|
| 72 |
+
| EasyOCRDetector | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 275.48 ms | 1 - 163 MB | NPU
|
| 73 |
+
| EasyOCRDetector | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 39.593 ms | 6 - 8 MB | NPU
|
| 74 |
+
| EasyOCRDetector | QNN_DLC | float | Qualcomm® SA8775P | 333.342 ms | 1 - 161 MB | NPU
|
| 75 |
+
| EasyOCRDetector | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 79.658 ms | 6 - 260 MB | NPU
|
| 76 |
+
| EasyOCRDetector | QNN_DLC | float | Qualcomm® SA7255P | 275.48 ms | 1 - 163 MB | NPU
|
| 77 |
+
| EasyOCRDetector | QNN_DLC | float | Qualcomm® SA8295P | 75.708 ms | 0 - 179 MB | NPU
|
| 78 |
+
| EasyOCRDetector | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 22.982 ms | 0 - 163 MB | NPU
|
| 79 |
+
| EasyOCRDetector | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 17.144 ms | 6 - 170 MB | NPU
|
| 80 |
+
| EasyOCRDetector | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 27.187 ms | 1 - 250 MB | NPU
|
| 81 |
+
| EasyOCRDetector | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 270.523 ms | 0 - 170 MB | NPU
|
| 82 |
+
| EasyOCRDetector | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 36.985 ms | 1 - 3 MB | NPU
|
| 83 |
+
| EasyOCRDetector | TFLITE | float | Qualcomm® SA8775P | 67.921 ms | 1 - 173 MB | NPU
|
| 84 |
+
| EasyOCRDetector | TFLITE | float | Qualcomm® QCS9075 | 70.05 ms | 0 - 49 MB | NPU
|
| 85 |
+
| EasyOCRDetector | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 77.087 ms | 1 - 253 MB | NPU
|
| 86 |
+
| EasyOCRDetector | TFLITE | float | Qualcomm® SA7255P | 270.523 ms | 0 - 170 MB | NPU
|
| 87 |
+
| EasyOCRDetector | TFLITE | float | Qualcomm® SA8295P | 73.836 ms | 1 - 179 MB | NPU
|
| 88 |
+
| EasyOCRDetector | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 21.938 ms | 1 - 171 MB | NPU
|
| 89 |
+
| EasyOCRDetector | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 16.49 ms | 1 - 173 MB | NPU
|
| 90 |
+
| EasyOCRDetector | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 7.289 ms | 0 - 214 MB | NPU
|
| 91 |
+
| EasyOCRDetector | TFLITE | w8a8 | Qualcomm® QCS6490 | 50.848 ms | 0 - 24 MB | NPU
|
| 92 |
+
| EasyOCRDetector | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 36.064 ms | 0 - 156 MB | NPU
|
| 93 |
+
| EasyOCRDetector | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 9.681 ms | 0 - 3 MB | NPU
|
| 94 |
+
| EasyOCRDetector | TFLITE | w8a8 | Qualcomm® SA8775P | 11.047 ms | 0 - 156 MB | NPU
|
| 95 |
+
| EasyOCRDetector | TFLITE | w8a8 | Qualcomm® QCS9075 | 11.451 ms | 0 - 24 MB | NPU
|
| 96 |
+
| EasyOCRDetector | TFLITE | w8a8 | Qualcomm® QCM6690 | 233.472 ms | 0 - 210 MB | NPU
|
| 97 |
+
| EasyOCRDetector | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 14.908 ms | 0 - 209 MB | NPU
|
| 98 |
+
| EasyOCRDetector | TFLITE | w8a8 | Qualcomm® SA7255P | 36.064 ms | 0 - 156 MB | NPU
|
| 99 |
+
| EasyOCRDetector | TFLITE | w8a8 | Qualcomm® SA8295P | 19.114 ms | 0 - 152 MB | NPU
|
| 100 |
+
| EasyOCRDetector | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 5.789 ms | 0 - 157 MB | NPU
|
| 101 |
+
| EasyOCRDetector | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 19.362 ms | 0 - 174 MB | NPU
|
| 102 |
+
| EasyOCRDetector | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 4.335 ms | 0 - 161 MB | NPU
|
| 103 |
+
| EasyOCRRecognizer | ONNX | float | Snapdragon® X Elite | 28.731 ms | 10 - 10 MB | NPU
|
| 104 |
+
| EasyOCRRecognizer | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 21.212 ms | 0 - 434 MB | NPU
|
| 105 |
+
| EasyOCRRecognizer | ONNX | float | Qualcomm® QCS8550 (Proxy) | 28.218 ms | 0 - 15 MB | NPU
|
| 106 |
+
| EasyOCRRecognizer | ONNX | float | Qualcomm® QCS9075 | 31.354 ms | 0 - 3 MB | NPU
|
| 107 |
+
| EasyOCRRecognizer | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 16.956 ms | 0 - 378 MB | NPU
|
| 108 |
+
| EasyOCRRecognizer | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 15.623 ms | 0 - 405 MB | NPU
|
| 109 |
+
| EasyOCRRecognizer | QNN_DLC | float | Snapdragon® X Elite | 16.274 ms | 0 - 0 MB | NPU
|
| 110 |
+
| EasyOCRRecognizer | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 12.143 ms | 0 - 785 MB | NPU
|
| 111 |
+
| EasyOCRRecognizer | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 47.77 ms | 0 - 670 MB | NPU
|
| 112 |
+
| EasyOCRRecognizer | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 16.494 ms | 0 - 3 MB | NPU
|
| 113 |
+
| EasyOCRRecognizer | QNN_DLC | float | Qualcomm® SA8775P | 21.009 ms | 0 - 672 MB | NPU
|
| 114 |
+
| EasyOCRRecognizer | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 22.017 ms | 0 - 389 MB | NPU
|
| 115 |
+
| EasyOCRRecognizer | QNN_DLC | float | Qualcomm® SA7255P | 47.77 ms | 0 - 670 MB | NPU
|
| 116 |
+
| EasyOCRRecognizer | QNN_DLC | float | Qualcomm® SA8295P | 24.836 ms | 0 - 335 MB | NPU
|
| 117 |
+
| EasyOCRRecognizer | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 10.358 ms | 0 - 671 MB | NPU
|
| 118 |
+
| EasyOCRRecognizer | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 10.514 ms | 0 - 732 MB | NPU
|
| 119 |
+
| EasyOCRRecognizer | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 75.235 ms | 17 - 27 MB | CPU
|
| 120 |
+
| EasyOCRRecognizer | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 354.574 ms | 1 - 10 MB | CPU
|
| 121 |
+
| EasyOCRRecognizer | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 78.386 ms | 6 - 8 MB | CPU
|
| 122 |
+
| EasyOCRRecognizer | TFLITE | float | Qualcomm® SA8775P | 164.135 ms | 9 - 15 MB | CPU
|
| 123 |
+
| EasyOCRRecognizer | TFLITE | float | Qualcomm® QCS9075 | 132.963 ms | 4 - 33 MB | CPU
|
| 124 |
+
| EasyOCRRecognizer | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 92.185 ms | 6 - 18 MB | CPU
|
| 125 |
+
| EasyOCRRecognizer | TFLITE | float | Qualcomm® SA7255P | 354.574 ms | 1 - 10 MB | CPU
|
| 126 |
+
| EasyOCRRecognizer | TFLITE | float | Qualcomm® SA8295P | 126.378 ms | 7 - 12 MB | CPU
|
| 127 |
+
| EasyOCRRecognizer | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 79.698 ms | 7 - 15 MB | CPU
|
| 128 |
+
| EasyOCRRecognizer | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 52.873 ms | 11 - 21 MB | CPU
|
| 129 |
+
| EasyOCRRecognizer | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 54.104 ms | 5 - 15 MB | CPU
|
| 130 |
+
| EasyOCRRecognizer | TFLITE | w8a8 | Qualcomm® QCS6490 | 152.742 ms | 3 - 22 MB | CPU
|
| 131 |
+
| EasyOCRRecognizer | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 299.941 ms | 6 - 14 MB | CPU
|
| 132 |
+
| EasyOCRRecognizer | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 56.204 ms | 5 - 22 MB | CPU
|
| 133 |
+
| EasyOCRRecognizer | TFLITE | w8a8 | Qualcomm® SA8775P | 139.386 ms | 8 - 13 MB | CPU
|
| 134 |
+
| EasyOCRRecognizer | TFLITE | w8a8 | Qualcomm® QCS9075 | 105.864 ms | 3 - 22 MB | CPU
|
| 135 |
+
| EasyOCRRecognizer | TFLITE | w8a8 | Qualcomm® QCM6690 | 162.227 ms | 7 - 12 MB | CPU
|
| 136 |
+
| EasyOCRRecognizer | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 69.267 ms | 6 - 17 MB | CPU
|
| 137 |
+
| EasyOCRRecognizer | TFLITE | w8a8 | Qualcomm® SA7255P | 299.941 ms | 6 - 14 MB | CPU
|
| 138 |
+
| EasyOCRRecognizer | TFLITE | w8a8 | Qualcomm® SA8295P | 98.815 ms | 5 - 11 MB | CPU
|
| 139 |
+
| EasyOCRRecognizer | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 63.653 ms | 8 - 21 MB | CPU
|
| 140 |
+
| EasyOCRRecognizer | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 85.371 ms | 8 - 15 MB | CPU
|
| 141 |
+
| EasyOCRRecognizer | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 40.636 ms | 9 - 19 MB | CPU
|
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| 142 |
|
| 143 |
## License
|
| 144 |
* The license for the original implementation of EasyOCR can be found
|
| 145 |
[here](https://github.com/JaidedAI/EasyOCR/blob/master/LICENSE).
|
| 146 |
|
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|
| 147 |
## References
|
| 148 |
* [Source Model Implementation](https://github.com/JaidedAI/EasyOCR)
|
| 149 |
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|
| 150 |
## Community
|
| 151 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 152 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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tool-versions.yaml
DELETED
|
@@ -1,3 +0,0 @@
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|
| 1 |
-
tool_versions:
|
| 2 |
-
tflite:
|
| 3 |
-
tflite: 2.17.0
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