提交 015cb7f0 编写于 作者: 文幕地方's avatar 文幕地方

add indicator explain

上级 b63088b9
......@@ -30,12 +30,16 @@ The table recognition flow chart is as follows
## 2. Performance
We evaluated the algorithm on the PubTabNet<sup>[1]</sup> eval dataset, and the performance is as follows:
|Method|acc|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|
| --- | --- | --- |
| EDD<sup>[2]</sup> |x| 88.3 |
| TableRec-RARE(ours) |73.8%| 93.32 |
| SLANet(ours) | 76.2%| 94.98 |SLANet |
|Method|Acc|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|Speed|
| --- | --- | --- | ---|
| EDD<sup>[2]</sup> |x| 88.3 |x|
| TableRec-RARE(ours) |73.8%| 93.32 |1180ms|
| SLANet(ours) | 76.2%| 94.98 |590ms|
The performance indicators are explained as follows:
- Acc: The accuracy of the table structure in each image, a wrong token is considered an error.
- TEDS: The accuracy of the model's restoration of table information. This indicator evaluates not only the table structure, but also the text content in the table.
- Speed: The inference speed of a single image when the model runs on the CPU machine and MKL is enabled.
## 3. Result
......
......@@ -36,11 +36,16 @@
我们在 PubTabNet<sup>[1]</sup> 评估数据集上对算法进行了评估,性能如下
|算法|acc|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|
| --- | --- | --- |
| EDD<sup>[2]</sup> |x| 88.3 |
| TableRec-RARE(ours) |73.8%| 93.32 |
| SLANet(ours) | 76.2%| 94.98 |
|算法|Acc|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|Speed|
| --- | --- | --- | ---|
| EDD<sup>[2]</sup> |x| 88.3 |x|
| TableRec-RARE(ours) |73.8%| 93.32 |1180ms|
| SLANet(ours) | 76.2%| 94.98 |590ms|
性能指标解释如下:
- 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。
- TEDS: 模型对表格信息还原的准确度,此指标评价内容不仅包含表格结构,还包含表格内的文字内容。
- Speed: 模型在CPU机器上,开启MKL的情况下,单张图片的推理速度。
## 3. 效果演示
......
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