update speed
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@ -33,8 +33,8 @@ We evaluated the algorithm on the PubTabNet<sup>[1]</sup> eval dataset, and the
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|Method|Acc|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|Speed|
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| --- | --- | --- | ---|
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| EDD<sup>[2]</sup> |x| 88.3 |x|
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| TableRec-RARE(ours) |73.8%| 93.32 |1180ms|
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| SLANet(ours) | 76.2%| 94.98 |590ms|
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| TableRec-RARE(ours) |73.8%| 93.32 |1550ms|
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| SLANet(ours) | 76.2%| 94.98 |766ms|
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The performance indicators are explained as follows:
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- Acc: The accuracy of the table structure in each image, a wrong token is considered an error.
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@ -39,8 +39,8 @@
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|算法|Acc|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|Speed|
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| --- | --- | --- | ---|
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| EDD<sup>[2]</sup> |x| 88.3 |x|
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| TableRec-RARE(ours) |73.8%| 93.32 |1180ms|
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| SLANet(ours) | 76.2%| 94.98 |590ms|
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| TableRec-RARE(ours) |73.8%| 93.32 |1550ms|
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| SLANet(ours) | 76.2%| 94.98 |766ms|
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性能指标解释如下:
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- Acc: 模型对每张图像里表格结构的识别准确率,错一个token就算错误。
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