commissarma / pytorch-craft Goto Github PK
View Code? Open in Web Editor NEW本项目旨在以CRAFT提供的预训练模型为基础,进行迁移学习以用于检测自己数据集中的文本。
本项目旨在以CRAFT提供的预训练模型为基础,进行迁移学习以用于检测自己数据集中的文本。
mini = dst[np.where(dst>0)].min()
在执行generate_score_map.py时出现了错误。
下面是某一张出错的图片等label数据,是仿照您提供的blw_0.json的格式制作的。
{
"imageHeight": 415,
"imagePath": "train_ReCTS_000024.jpg",
"imageWidth": 307,
"shapes": [
{
"label": "舊",
"points": [
[
90,
80
],
[
152,
80
],
[
152,
144
],
[
90,
144
]
]
},
{
"label": "街",
"points": [
[
155,
76
],
[
223,
76
],
[
223,
140
],
[
155,
140
]
]
},
{
"label": "糖",
"points": [
[
132,
156
],
[
176,
156
],
[
176,
198
],
[
132,
198
]
]
},
{
"label": "水",
"points": [
[
132,
200
],
[
175,
200
],
[
175,
241
],
[
132,
241
]
]
},
{
"label": "铺",
"points": [
[
132,
243
],
[
176,
243
],
[
176,
284
],
[
132,
284
]
]
}
]
}
您好,我的文本大概都是像收據有一格一格的
如果我想訓練自己的數據集,我是要整個收據去標註
還是要把收據切成小張圖片(一行)再去標註,謝謝
@CommissarMa Thank you for your hard work,
Will you consider integrating OCR to recognize the detected lines.
您好,咨询一下,在用自己的数据做训练,大概什么样的数据量能够有你描述的那样,有一个比较好的提升?
你好我训练了双层黄色车牌效果不是很好,双层黄色车牌是那种大卡车车牌,比如车牌上面是京A,下面是56G89。我训练得图片是把车牌直接裁剪下来,通过labelme标注,一共100张做训练,但是训练完每次识别都是整个车牌,我想检测车牌上面得京A,和下面得56G89。
@CommissarMa
Will you consider adding the ability to extract the detected boxes.
Basically, crop the detected boxes and save each crop as an image.
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