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子豪兄你好,很感谢你无私分享ai干货,我在学习你的代码时发现一个小bug。 在关键点检测-3-YOLOV8关键点检测-预训练模型预测-【c1】YOLOV8预训练模型预测-Python API-图像.ipynb文件中 In[25]中,代码: # 获取框的预测类别(对于关键点检测,只有一个类别) bbox_label = results[0].names[0] results[0].names获取的是coco.yaml文件中所有names的数组形式,所以results[0].names[0]这种取值方式获取到的不是结果类别,而永远是names数组中的第一个对象,也就是person。
我使用以下代码进行了修改: proto = tf.make_tensor_proto(results[0].boxes.cls) array = tf.make_ndarray(proto) bbox_label = results[0].names[array[idx]] 以获取到获取框的预测类别
The text was updated successfully, but these errors were encountered:
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子豪兄你好,很感谢你无私分享ai干货,我在学习你的代码时发现一个小bug。
在关键点检测-3-YOLOV8关键点检测-预训练模型预测-【c1】YOLOV8预训练模型预测-Python API-图像.ipynb文件中 In[25]中,代码:
# 获取框的预测类别(对于关键点检测,只有一个类别)
bbox_label = results[0].names[0]
results[0].names获取的是coco.yaml文件中所有names的数组形式,所以results[0].names[0]这种取值方式获取到的不是结果类别,而永远是names数组中的第一个对象,也就是person。
我使用以下代码进行了修改:
proto = tf.make_tensor_proto(results[0].boxes.cls)
array = tf.make_ndarray(proto)
bbox_label = results[0].names[array[idx]]
以获取到获取框的预测类别
The text was updated successfully, but these errors were encountered: