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@@ -21,20 +21,52 @@ The text detection module is a crucial component in OCR (Optical Character Recog
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</thead>
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<tbody>
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<tr>
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+<td>PP-OCRv5_server_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_server_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_server_det_pretrained.pdparams">Training Model</a></td>
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+<td>83.8</td>
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+<td>- / -</td>
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+<td>- / -</td>
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+<td>101</td>
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+<td>PP-OCRv5 server-side text detection model with higher accuracy, suitable for deployment on high-performance servers</td>
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+</tr>
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+<tr>
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+<td>PP-OCRv5_mobile_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv5_mobile_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv5_mobile_det_pretrained.pdparams">Training Model</a></td>
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+<td>79.0</td>
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+<td>- / -</td>
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+<td>- / -</td>
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+<td>4.7</td>
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+<td>PP-OCRv5 mobile-side text detection model with higher efficiency, suitable for deployment on edge devices</td>
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+</tr>
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+<tr>
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<td>PP-OCRv4_server_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_server_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_server_det_pretrained.pdparams">Training Model</a></td>
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-<td>82.69</td>
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+<td>69.2</td>
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<td>83.34 / 80.91</td>
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<td>442.58 / 442.58</td>
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<td>109</td>
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-<td>The server-side text detection model of PP-OCRv4, featuring higher accuracy and suitable for deployment on high-performance servers</td>
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+<td>PP-OCRv4 server-side text detection model with higher accuracy, suitable for deployment on high-performance servers</td>
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</tr>
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<tr>
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<td>PP-OCRv4_mobile_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv4_mobile_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv4_mobile_det_pretrained.pdparams">Training Model</a></td>
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-<td>77.79</td>
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+<td>63.8</td>
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<td>8.79 / 3.13</td>
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<td>51.00 / 28.58</td>
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<td>4.7</td>
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-<td>The mobile text detection model of PP-OCRv4, optimized for efficiency and suitable for deployment on edge devices</td>
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+<td>PP-OCRv4 mobile-side text detection model with higher efficiency, suitable for deployment on edge devices</td>
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+</tr>
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+<tr>
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+<td>PP-OCRv3_mobile_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv3_mobile_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv3_mobile_det_pretrained.pdparams">Training Model</a></td>
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+<td>Accuracy comparable to PP-OCRv4_mobile_det</td>
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+<td>8.44 / 2.91</td>
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+<td>27.87 / 27.87</td>
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+<td>2.1</td>
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+<td>PP-OCRv3 mobile text detection model with higher efficiency, suitable for edge device deployment</td>
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+</tr>
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+<tr>
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+<td>PP-OCRv3_server_det</td><td><a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_inference_model/paddle3.0.0/PP-OCRv3_server_det_infer.tar">Inference Model</a>/<a href="https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-OCRv3_server_det_pretrained.pdparams">Training Model</a></td>
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+<td>Accuracy comparable to PP-OCRv4_server_det</td>
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+<td>65.41 / 13.67</td>
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+<td>305.07 / 305.07</td>
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+<td>102.1</td>
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+<td>PP-OCRv3 server text detection model with higher accuracy, suitable for deployment on high-performance servers</td>
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</tr>
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</tbody>
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</table>
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@@ -44,7 +76,7 @@ The text detection module is a crucial component in OCR (Optical Character Recog
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<ul>
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<li><b>Performance Test Environment</b>
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<ul>
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- <li><strong>Test Dataset：</strong> PaddleOCR’s self-built Chinese and English dataset covers multiple scenarios including street views, web images, documents, and handwriting. The Chinese text detection dataset contains 593 images.</li>
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+ <li><strong>Test Dataset：</strong>PaddleOCR3.0 newly constructed multilingual dataset (including Chinese, Traditional Chinese, English, Japanese), covering street scenes, web images, documents, handwriting, blur, rotation, distortion, etc., totaling 2677 images.</li>
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<li><strong>Hardware Configuration：</strong>
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<ul>
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<li>GPU: NVIDIA Tesla T4</li>
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@@ -89,7 +121,7 @@ Just a few lines of code can complete the inference of the text detection module
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```python
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from paddlex import create_model
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-model = create_model(model_name="PP-OCRv4_mobile_det")
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+model = create_model(model_name="PP-OCRv5_mobile_det")
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output = model.predict("general_ocr_001.png", batch_size=1)
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for res in output:
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res.print()
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@@ -100,7 +132,15 @@ for res in output:
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After running, the result obtained is:
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```bash
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-{'res': {'input_path': 'general_ocr_001.png', "page_index": None, 'dt_polys': [[[73, 552], [453, 542], [454, 575], [74, 585]], [[17, 506], [515, 486], [517, 535], [19, 555]], [[189, 457], [398, 449], [399, 482], [190, 490]], [[41, 412], [484, 387], [486, 433], [43, 457]]], 'dt_scores': [0.7555687038101032, 0.701620896397861, 0.8839516283528792, 0.8123399529333318]}}
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+{'res': {'input_path': 'general_ocr_001.png', 'page_index': None, 'dt_polys': array([[[ 75, 549],
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+ ...,
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+ [ 77, 586]],
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+
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+ ...,
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+
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+ [[ 37, 408],
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+ ...,
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+ [ 39, 453]]], dtype=int16), 'dt_scores': [0.832930755107492, 0.8186143846140158, 0.8591595100376676, 0.8718863959111733]}}
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```
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The meanings of the running result parameters are as follows:
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@@ -117,7 +157,7 @@ Note: Due to network issues, the above URL may not be successfully parsed. If yo
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Relevant methods, parameters, and explanations are as follows:
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-* `create_model` instantiates a text detection model (here using `PP-OCRv4_mobile_det` as an example). The specific explanation is as follows:
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+* `create_model` instantiates a text detection model (here using `PP-OCRv5_mobile_det` as an example). The specific explanation is as follows:
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<table>
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<thead>
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<tr>
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@@ -420,7 +460,7 @@ tar -xf ./dataset/ocr_det_dataset_examples.tar -C ./dataset/
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A single command can complete data validation:
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```bash
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-python main.py -c paddlex/configs/modules/text_detection/PP-OCRv4_mobile_det.yaml \
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+python main.py -c paddlex/configs/modules/text_detection/PP-OCRv5_mobile_det.yaml \
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-o Global.mode=check_dataset \
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-o Global.dataset_dir=./dataset/ocr_det_dataset_examples
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```
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@@ -488,13 +528,13 @@ CheckDataset:
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......
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</code></pre>
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<p>Then execute the command:</p>
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-<pre><code class="language-bash">python main.py -c paddlex/configs/modules/text_detection/PP-OCRv4_mobile_det.yaml \
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+<pre><code class="language-bash">python main.py -c paddlex/configs/modules/text_detection/PP-OCRv5_mobile_det.yaml \
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-o Global.mode=check_dataset \
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-o Global.dataset_dir=./dataset/ocr_det_dataset_examples
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</code></pre>
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<p>After dataset splitting, the original annotation files will be renamed to <code>xxx.bak</code> in the original path.</p>
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<p>The above parameters can also be set by appending command-line arguments:</p>
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-<pre><code class="language-bash">python main.py -c paddlex/configs/modules/text_detection/PP-OCRv4_mobile_det.yaml \
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+<pre><code class="language-bash">python main.py -c paddlex/configs/modules/text_detection/PP-OCRv5_mobile_det.yaml \
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-o Global.mode=check_dataset \
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-o Global.dataset_dir=./dataset/ocr_det_dataset_examples \
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-o CheckDataset.split.enable=True \
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@@ -503,16 +543,16 @@ CheckDataset:
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</code></pre></details>
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### 4.2 Model Training
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-Model training can be completed with a single command. Here's an example of training the PP-OCRv4 mobile text detection model (`PP-OCRv4_mobile_det`):
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+Model training can be completed with a single command. Here's an example of training the PP-OCRv5 mobile text detection model (`PP-OCRv5_mobile_det`):
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```bash
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-python main.py -c paddlex/configs/modules/text_detection/PP-OCRv4_mobile_det.yaml \
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+python main.py -c paddlex/configs/modules/text_detection/PP-OCRv5_mobile_det.yaml \
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-o Global.mode=train \
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-o Global.dataset_dir=./dataset/ocr_det_dataset_examples
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```
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The steps required are:
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-* Specify the path to the model's `.yaml` configuration file (here it's `PP-OCRv4_mobile_det.yaml`. When training other models, you need to specify the corresponding configuration files. The relationship between the model and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list.en.md))
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+* Specify the path to the model's `.yaml` configuration file (here it's `PP-OCRv5_mobile_det.yaml`. When training other models, you need to specify the corresponding configuration files. The relationship between the model and configuration files can be found in the [PaddleX Model List (CPU/GPU)](../../../support_list/models_list.en.md))
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* Set the mode to model training: `-o Global.mode=train`
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* Specify the path to the training dataset: `-o Global.dataset_dir`
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* Other related parameters can be set by modifying the `Global` and `Train` fields in the `.yaml` configuration file or adjusted by appending parameters in the command line. For example, to specify training on the first two GPUs: `-o Global.device=gpu:0,1`; to set the number of training epochs to 10: `-o Train.epochs_iters=10`. For more modifiable parameters and their detailed explanations, refer to the [PaddleX Common Configuration Parameters Documentation](../../../module_usage/instructions/config_parameters_common.en.md).
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@@ -539,14 +579,14 @@ The steps required are:
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After completing model training, you can evaluate the specified model weight file on the validation set to verify the model's accuracy. Using PaddleX for model evaluation can be done with a single command:
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```bash
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-python main.py -c paddlex/configs/modules/text_detection/PP-OCRv4_mobile_det.yaml \
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+python main.py -c paddlex/configs/modules/text_detection/PP-OCRv5_mobile_det.yaml \
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-o Global.mode=evaluate \
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-o Global.dataset_dir=./dataset/ocr_det_dataset_examples
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```
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Similar to model training, the following steps are required:
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-* Specify the path to the model's `.yaml` configuration file (in this case, `PP-OCRv4_mobile_det.yaml`)
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+* Specify the path to the model's `.yaml` configuration file (in this case, `PP-OCRv5_mobile_det.yaml`)
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* Specify the mode as model evaluation: `-o Global.mode=evaluate`
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* Specify the path to the validation dataset: `-o Global.dataset_dir`
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@@ -563,14 +603,14 @@ After completing model training and evaluation, you can use the trained model we
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To perform inference predictions via the command line, simply use the following command. Before running the following code, please download the [demo image](https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_ocr_001.png) to your local machine.
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```bash
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-python main.py -c paddlex/configs/modules/text_detection/PP-OCRv4_mobile_det.yaml \
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+python main.py -c paddlex/configs/modules/text_detection/PP-OCRv5_mobile_det.yaml \
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-o Global.mode=predict \
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-o Predict.model_dir="./output/best_accuracy/inference" \
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-o Predict.input="general_ocr_001.png"
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```
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Similar to model training and evaluation, the following steps are required:
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-* Specify the `.yaml` configuration file path of the model (here it's `PP-OCRv4_mobile_det.yaml`)
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+* Specify the `.yaml` configuration file path of the model (here it's `PP-OCRv5_mobile_det.yaml`)
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* Set the mode to model inference prediction: `-o Global.mode=predict`
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* Specify the model weights path: `-o Predict.model_dir="./output/best_accuracy/inference"`
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* Specify the input data path: `-o Predict.input="..."`
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