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@ -53,11 +53,10 @@ PP-OCRv3检测模型是对PP-OCRv2中的[CML](https://arxiv.org/pdf/2109.03144.p
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|序号|策略|模型大小|hmean|速度(cpu + mkldnn)|
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|baseline teacher|PP-OCR server|49M|83.2%|171ms|
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|baseline teacher|DB-R50|99M|83.5%|260ms|
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|teacher1|DB-R50-LK-PAN|124M|85.0%|396ms|
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|teacher2|DB-R50-LK-PAN-DML|124M|86.0%|396ms|
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|baseline student|PP-OCRv2|3M|83.2%|117ms|
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|student0|DB-MV3-RSE-FPN|3.6M|84.5%|124ms|
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|student1|DB-MV3-CML(teacher2)|3M|84.3%|117ms|
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|student2|DB-MV3-RSE-FPN-CML(teacher2)|3.6M|85.4%|124ms|
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@ -55,11 +55,10 @@ The ablation experiments are as follows:
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|ID|Strategy|Model Size|Hmean|The Inference Time(cpu + mkldnn)|
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|baseline teacher|PP-OCR server|49M|83.2%|171ms|
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|baseline teacher|DB-R50|99M|83.5%|260ms|
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|teacher1|DB-R50-LK-PAN|124M|85.0%|396ms|
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|teacher2|DB-R50-LK-PAN-DML|124M|86.0%|396ms|
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|baseline student|PP-OCRv2|3M|83.2%|117ms|
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|student0|DB-MV3-RSE-FPN|3.6M|84.5%|124ms|
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|student1|DB-MV3-CML(teacher2)|3M|84.3%|117ms|
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|student2|DB-MV3-RSE-FPN-CML(teacher2)|3.6M|85.4%|124ms|
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@ -438,7 +438,12 @@ class KieLabelEncode(object):
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texts.append(ann['transcription'])
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text_ind = [self.dict[c] for c in text if c in self.dict]
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text_inds.append(text_ind)
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labels.append(ann['label'])
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if 'label' in anno.keys():
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labels.append(ann['label'])
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elif 'key_cls' in anno.keys():
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labels.append(anno['key_cls'])
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else:
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raise ValueError("Cannot found 'key_cls' in ann.keys(), please check your training annotation.")
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edges.append(ann.get('edge', 0))
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ann_infos = dict(
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image=data['image'],
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