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--- |
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library_name: pytorch |
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license: apache-2.0 |
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tags: |
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- android |
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pipeline_tag: image-to-text |
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--- |
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# EasyOCR: Optimized for Mobile Deployment |
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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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This model is an implementation of EasyOCR found [here](https://github.com/JaidedAI/EasyOCR). |
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This repository provides scripts to run EasyOCR on Qualcomm® devices. |
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More details on model performance across various devices, can be found |
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[here](https://aihub.qualcomm.com/models/easyocr). |
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### Model Details |
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- **Model Type:** Image to text |
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- **Model Stats:** |
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- Model checkpoint: easyocr-small-stage1 |
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- Input resolution: 384x384 |
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- Number of parameters (EasyOCRDetector): 20.8M |
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- Model size (EasyOCRDetector): 79.2 MB |
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- Number of parameters (EasyOCRRecognizer): 3.84M |
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- Model size (EasyOCRRecognizer): 14.7 MB |
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| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
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|---|---|---|---|---|---|---|---|---| |
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| EasyOCRDetector | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 41.72 ms | 1 - 132 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.tflite) | |
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| EasyOCRDetector | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 37.7 ms | 6 - 17 MB | FP16 | NPU | [EasyOCR.so](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.so) | |
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| EasyOCRDetector | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 41.887 ms | 32 - 121 MB | FP16 | NPU | [EasyOCR.onnx](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.onnx) | |
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| EasyOCRDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 30.07 ms | 14 - 72 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.tflite) | |
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| EasyOCRDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 28.085 ms | 6 - 24 MB | FP16 | NPU | [EasyOCR.so](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.so) | |
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| EasyOCRDetector | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 30.704 ms | 42 - 74 MB | FP16 | NPU | [EasyOCR.onnx](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.onnx) | |
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| EasyOCRDetector | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 29.358 ms | 14 - 48 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.tflite) | |
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| EasyOCRDetector | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 28.786 ms | 6 - 33 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRDetector | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 24.189 ms | 40 - 68 MB | FP16 | NPU | [EasyOCR.onnx](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.onnx) | |
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| EasyOCRDetector | SA7255P ADP | SA7255P | TFLITE | 2113.984 ms | 0 - 30 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.tflite) | |
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| EasyOCRDetector | SA7255P ADP | SA7255P | QNN | 2109.673 ms | 1 - 10 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRDetector | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 41.306 ms | 9 - 140 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.tflite) | |
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| EasyOCRDetector | SA8255 (Proxy) | SA8255P Proxy | QNN | 38.731 ms | 6 - 8 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRDetector | SA8295P ADP | SA8295P | TFLITE | 78.453 ms | 16 - 49 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.tflite) | |
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| EasyOCRDetector | SA8295P ADP | SA8295P | QNN | 75.057 ms | 0 - 18 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRDetector | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 41.546 ms | 10 - 144 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.tflite) | |
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| EasyOCRDetector | SA8650 (Proxy) | SA8650P Proxy | QNN | 38.334 ms | 6 - 7 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRDetector | SA8775P ADP | SA8775P | TFLITE | 88.531 ms | 16 - 45 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.tflite) | |
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| EasyOCRDetector | SA8775P ADP | SA8775P | QNN | 84.933 ms | 1 - 11 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRDetector | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 2113.984 ms | 0 - 30 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.tflite) | |
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| EasyOCRDetector | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 2109.673 ms | 1 - 10 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRDetector | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 38.797 ms | 6 - 8 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRDetector | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 88.531 ms | 16 - 45 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.tflite) | |
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| EasyOCRDetector | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 84.933 ms | 1 - 11 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRDetector | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 82.831 ms | 16 - 77 MB | FP16 | NPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.tflite) | |
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| EasyOCRDetector | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 69.737 ms | 6 - 36 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRDetector | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 38.608 ms | 6 - 6 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRDetector | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 41.643 ms | 66 - 66 MB | FP16 | NPU | [EasyOCR.onnx](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRDetector.onnx) | |
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| EasyOCRRecognizer | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 117.587 ms | 3 - 6 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) | |
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| EasyOCRRecognizer | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 23.252 ms | 0 - 3 MB | FP16 | NPU | [EasyOCR.so](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.so) | |
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| EasyOCRRecognizer | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 20.753 ms | 0 - 22 MB | FP16 | NPU | [EasyOCR.onnx](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.onnx) | |
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| EasyOCRRecognizer | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 109.883 ms | 9 - 29 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) | |
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| EasyOCRRecognizer | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 16.886 ms | 0 - 18 MB | FP16 | NPU | [EasyOCR.so](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.so) | |
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| EasyOCRRecognizer | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 16.816 ms | 0 - 25 MB | FP16 | NPU | [EasyOCR.onnx](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.onnx) | |
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| EasyOCRRecognizer | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 106.439 ms | 20 - 35 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) | |
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| EasyOCRRecognizer | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 19.025 ms | 0 - 428 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRRecognizer | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 14.032 ms | 0 - 22 MB | FP16 | NPU | [EasyOCR.onnx](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.onnx) | |
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| EasyOCRRecognizer | SA7255P ADP | SA7255P | TFLITE | 571.291 ms | 8 - 18 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) | |
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| EasyOCRRecognizer | SA7255P ADP | SA7255P | QNN | 281.893 ms | 0 - 10 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRRecognizer | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 129.985 ms | 2 - 4 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) | |
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| EasyOCRRecognizer | SA8255 (Proxy) | SA8255P Proxy | QNN | 23.373 ms | 0 - 2 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRRecognizer | SA8295P ADP | SA8295P | TFLITE | 218.62 ms | 6 - 23 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) | |
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| EasyOCRRecognizer | SA8295P ADP | SA8295P | QNN | 39.157 ms | 0 - 18 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRRecognizer | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 125.209 ms | 7 - 10 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) | |
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| EasyOCRRecognizer | SA8650 (Proxy) | SA8650P Proxy | QNN | 23.218 ms | 0 - 3 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRRecognizer | SA8775P ADP | SA8775P | TFLITE | 410.407 ms | 11 - 21 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) | |
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| EasyOCRRecognizer | SA8775P ADP | SA8775P | QNN | 31.266 ms | 0 - 10 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRRecognizer | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 571.291 ms | 8 - 18 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) | |
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| EasyOCRRecognizer | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 281.893 ms | 0 - 10 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRRecognizer | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 116.297 ms | 0 - 38 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) | |
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| EasyOCRRecognizer | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 23.278 ms | 0 - 3 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRRecognizer | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 410.407 ms | 11 - 21 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) | |
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| EasyOCRRecognizer | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 31.266 ms | 0 - 10 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRRecognizer | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 164.69 ms | 5 - 27 MB | FP32 | CPU | [EasyOCR.tflite](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.tflite) | |
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| EasyOCRRecognizer | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 36.284 ms | 0 - 170 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRRecognizer | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 24.48 ms | 0 - 0 MB | FP16 | NPU | Use Export Script | |
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| EasyOCRRecognizer | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 17.61 ms | 0 - 0 MB | FP16 | NPU | [EasyOCR.onnx](https://huggingface.co/qualcomm/EasyOCR/blob/main/EasyOCRRecognizer.onnx) | |
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## Installation |
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Install the package via pip: |
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```bash |
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pip install "qai-hub-models[easyocr]" |
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``` |
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## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device |
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Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your |
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Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. |
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With this API token, you can configure your client to run models on the cloud |
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hosted devices. |
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```bash |
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qai-hub configure --api_token API_TOKEN |
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``` |
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Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. |
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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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``` |
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Profiling Results |
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------------------------------------------------------------ |
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EasyOCRDetector |
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Device : Samsung Galaxy S23 (13) |
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Runtime : TFLITE |
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Estimated inference time (ms) : 41.7 |
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Estimated peak memory usage (MB): [1, 132] |
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Total # Ops : 42 |
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Compute Unit(s) : NPU (42 ops) |
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------------------------------------------------------------ |
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EasyOCRRecognizer |
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Device : Samsung Galaxy S23 (13) |
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Runtime : TFLITE |
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Estimated inference time (ms) : 117.6 |
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Estimated peak memory usage (MB): [3, 6] |
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Total # Ops : 136 |
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Compute Unit(s) : CPU (136 ops) |
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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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model = Model.from_pretrained() |
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detector_model = model.detector |
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recognizer_model = model.recognizer |
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# Device |
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device = hub.Device("Samsung Galaxy S23") |
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# Trace model |
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detector_input_shape = detector_model.get_input_spec() |
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detector_sample_inputs = detector_model.sample_inputs() |
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traced_detector_model = torch.jit.trace(detector_model, [torch.tensor(data[0]) for _, data in detector_sample_inputs.items()]) |
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# Compile model on a specific device |
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detector_compile_job = hub.submit_compile_job( |
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model=traced_detector_model , |
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device=device, |
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input_specs=detector_model.get_input_spec(), |
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) |
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# Get target model to run on-device |
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detector_target_model = detector_compile_job.get_target_model() |
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# Trace model |
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recognizer_input_shape = recognizer_model.get_input_spec() |
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recognizer_sample_inputs = recognizer_model.sample_inputs() |
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traced_recognizer_model = torch.jit.trace(recognizer_model, [torch.tensor(data[0]) for _, data in recognizer_sample_inputs.items()]) |
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# Compile model on a specific device |
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recognizer_compile_job = hub.submit_compile_job( |
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model=traced_recognizer_model , |
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device=device, |
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input_specs=recognizer_model.get_input_spec(), |
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) |
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# Get target model to run on-device |
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recognizer_target_model = recognizer_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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After compiling models from step 1. Models can be profiled model on-device using the |
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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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detector_profile_job = hub.submit_profile_job( |
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model=detector_target_model, |
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device=device, |
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) |
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recognizer_profile_job = hub.submit_profile_job( |
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model=recognizer_target_model, |
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device=device, |
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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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detector_input_data = detector_model.sample_inputs() |
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detector_inference_job = hub.submit_inference_job( |
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model=detector_target_model, |
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device=device, |
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inputs=detector_input_data, |
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) |
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detector_inference_job.download_output_data() |
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recognizer_input_data = recognizer_model.sample_inputs() |
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recognizer_inference_job = hub.submit_inference_job( |
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model=recognizer_target_model, |
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device=device, |
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inputs=recognizer_input_data, |
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) |
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recognizer_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. [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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* 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) |
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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:[email protected]). |
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