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@@ -29,19 +29,18 @@ paddlex.det.visualize(image, result, threshold=0.5, save_dir='./', color=None)
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使用示例：
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-```
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+```python
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import paddlex as pdx
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model = pdx.load_model('xiaoduxiong_epoch_12')
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result = model.predict('./xiaoduxiong_epoch_12/xiaoduxiong.jpeg')
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pdx.det.visualize('./xiaoduxiong_epoch_12/xiaoduxiong.jpeg', result, save_dir='./')
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# 预测结果保存在./visualize_xiaoduxiong.jpeg
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-
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```
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## <h2 id="2">paddlex.det.draw_pr_curve</h2>
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> 目标检测/实例分割准确率-召回率可视化
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-```
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+```python
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paddlex.det.draw_pr_curve(eval_details_file=None, gt=None, pred_bbox=None, pred_mask=None, iou_thresh=0.5, save_dir='./')
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```
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将目标检测/实例分割模型评估结果中各个类别的准确率和召回率的对应关系进行可视化，同时可视化召回率和置信度阈值的对应关系。
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@@ -61,16 +60,15 @@ paddlex.det.draw_pr_curve(eval_details_file=None, gt=None, pred_bbox=None, pred_
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点击下载如下示例中的[模型](https://bj.bcebos.com/paddlex/2.0/faster_rcnn_e12.tar.gz)和[数据集](https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz)
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> 方式一：分析训练过程中保存的模型文件夹中的评估结果文件`eval_details.json`，例如[模型](https://bj.bcebos.com/paddlex/models/insect_epoch_270.zip)中的`eval_details.json`。
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-```
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+```python
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import paddlex as pdx
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eval_details_file = 'faster_rcnn_e12/eval_details.json'
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pdx.det.draw_pr_curve(eval_details_file, save_dir='./insect')
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```
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> 方式二：分析模型评估函数返回的评估结果。
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-```
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+```python
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import paddlex as pdx
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-from paddlex import transforms as T
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model = pdx.load_model('faster_rcnn_e12')
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eval_dataset = pdx.datasets.VOCDetection(
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@@ -91,7 +89,7 @@ pdx.det.draw_pr_curve(gt=gt, pred_bbox=bbox, save_dir='./insect')
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## <h2 id="3">paddlex.det.coco_error_analysis</h2>
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> 分析模型预测错误的原因
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-```
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+```python
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paddlex.det.coco_error_analysis(eval_details_file=None, gt=None, pred_bbox=None, pred_mask=None, save_dir='./output')
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```
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逐个分析模型预测错误的原因，并将分析结果以图表的形式展示。分析结果图表示例如下：
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@@ -125,16 +123,15 @@ paddlex.det.coco_error_analysis(eval_details_file=None, gt=None, pred_bbox=None,
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点击下载如下示例中的[模型](https://bj.bcebos.com/paddlex/models/insect_epoch_270.zip)和[数据集](https://bj.bcebos.com/paddlex/datasets/insect_det.tar.gz)
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> 方式一：分析训练过程中保存的模型文件夹中的评估结果文件`eval_details.json`，例如[模型](https://bj.bcebos.com/paddlex/models/insect_epoch_270.zip)中的`eval_details.json`。
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-```
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+```python
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import paddlex as pdx
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eval_details_file = 'insect_epoch_270/eval_details.json'
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pdx.det.coco_error_analysis(eval_details_file, save_dir='./insect')
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```
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> 方式二：分析模型评估函数返回的评估结果。
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-```
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+```python
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import paddlex as pdx
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-from paddlex import transforms as T
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model = pdx.load_model('insect_epoch_270')
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eval_dataset = pdx.datasets.VOCDetection(
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@@ -171,7 +168,7 @@ paddlex.seg.visualize(image, result, weight=0.6, save_dir='./', color=None)
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使用示例：
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-```
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+```python
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import paddlex as pdx
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model = pdx.load_model('cityscape_deeplab')
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result = model.predict('city.png')
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will-jl944