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-## Text Detect Metric
-<p align="left">
-     <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-pink.svg"></a>
-     <a href=""><img src="https://img.shields.io/badge/python->=3.6,<3.12-aff.svg"></a>
-     <a href="https://pypi.org/project/text_det_metric/"><img alt="PyPI" src="https://img.shields.io/pypi/v/text_det_metric"></a>
-     <a href="https://pepy.tech/project/text_det_metric"><img src="https://static.pepy.tech/personalized-badge/text_det_metric?period=total&units=abbreviation&left_color=grey&right_color=blue&left_text=Downloads "></a>
-<a href="https://semver.org/"><img alt="SemVer2.0" src="https://img.shields.io/badge/SemVer-2.0-brightgreen"></a>
-     <a href="https://github.com/psf/black"><img src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
-</p>
-
-
-- This library is used to calculate the three metric `Precision`, `Recall` and `H-mean` to evaluate the effect of text detection algorithms. It is used in conjunction with [Modelscope-Text Detection Test Set](https://www.modelscope.cn/datasets/liekkas/text_det_test_dataset/summary).
-- Indicator calculation code reference: [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR/blob/b13f99607653c220ba94df2a8650edac086b0f37/ppocr/metrics/eval_det_iou.py) and [DB](https://github.com/MhLiao/DB/blob/3c32b808d4412680310d3d28eeb6a2d5bf1566c5/concern/icdar2015_eval/detection/iou.py#L8)
-
-
-### Evaluate on the custom dataset.
-- Here we use the evaluation code of `ch_ppocr_v3_det` on the text detection test set [liekkas/text_det_test_dataset](https://www.modelscope.cn/datasets/liekkas/text_det_test_dataset/summary), and you can use the same analogy.
-
-
-### Usage
-1. Install packages.
-    ```bash
-    pip install modelscope==1.5.2
-    pip install text_det_metric
-    ```
-2. Run `get_pred_txt.py` to get `pred.txt`
-    <details>
-    <summary>Click to expand</summary>
-
-    ```python
-    from pathlib import Path
-
-    import cv2
-    import yaml
-    from modelscope.msdatasets import MsDataset
-    from tqdm import tqdm
-
-    from det_demos.ch_ppocr_v3_det import TextDetector
-
-    root_dir = Path(__file__).resolve().parent
-
-
-    def read_yaml(yaml_path):
-        with open(yaml_path, "rb") as f:
-            data = yaml.load(f, Loader=yaml.Loader)
-        return data
-
-
-    test_data = MsDataset.load(
-        "text_det_test_dataset",
-        namespace="liekkas",
-        subset_name="default",
-        split="test",
-    )
-
-    config_path = root_dir / 'det_demos' / 'ch_ppocr_v3_det' / 'config.yaml'
-    config = read_yaml(str(config_path))
-
-    # Configure the onnx model path.
-    config['model_path'] = str(root_dir / 'det_demos' / config['model_path'])
-
-    text_detector = TextDetector(config)
-
-    content = []
-    for one_data in tqdm(test_data):
-        img_path = one_data.get("image:FILE")
-
-        img = cv2.imread(str(img_path))
-        dt_boxes, elapse = text_detector(img)
-        content.append(f"{img_path}\t{dt_boxes.tolist()}\t{elapse}")
-
-    with open("pred.txt", "w", encoding="utf-8") as f:
-        for v in content:
-            f.write(f"{v}\n")
-    ```
-    </details>
-3. Run `compute_metric.py` to get the metrics on the dataset
-    ```python
-    from text_det_metric import DetectionIoUEvaluator
-
-    metric = DetectionIoUEvaluator()
-
-    # pred_path
-    pred_path = "pred.txt"
-    metric = metric(pred_path)
-    print(metric)
-    ```
-4. Output
-    ```python
-    {
-        'precision': 0.6958333333333333,
-        'recall': 0.8608247422680413,
-        'hmean': 0.7695852534562212,
-        'avg_elapse': 2.0107483345529307
-    }
-    ```
-
 ### See details for [TextDetMetric](https://github.com/SWHL/TextDetMetric).