Update README.md

README.md

@@ -27,12 +27,12 @@ These model is used for monochrome image classification, based on CNNs and Trans
 
 The following are the checkpoints that have been formally put into use, all based on the Caformer architecture:
 
-|          Checkpoint          | Algorithm |  Accuracy  | False Negative | False Positive |
-|:----------------------------:|:---------:|:----------:|:--------------:|:--------------:|
-|    monochrome-caformer-40    |  caformer |   96.41%   |      2.69%     |      0.89%     |
-|  **monochrome-caformer-110** |  caformer | **96.97%** |      1.57%     |      1.46%     |
-| monochrome-caformer_safe2-80 |  caformer |   94.84%   |    **1.12%**   |      4.03%     |
-| monochrome-caformer_safe4-70 |  caformer |   94.28%   |    **0.67%**   |      5.04%     |
+|          Checkpoint          | Algorithm | Safe Level |  Accuracy  | False Negative | False Positive |
+|:----------------------------:|:---------:|:----------:|:----------:|:--------------:|:--------------:|
+|    monochrome-caformer-40    |  caformer |      0     |   96.41%   |      2.69%     |      0.89%     |
+|  **monochrome-caformer-110** |  caformer |      0     | **96.97%** |      1.57%     |      1.46%     |
+| monochrome-caformer_safe2-80 |  caformer |      2     |   94.84%   |    **1.12%**   |      4.03%     |
+| monochrome-caformer_safe4-70 |  caformer |      4     |   94.28%   |    **0.67%**   |      5.04%     |
 
 **`monochrome-caformer-110` has the best overall accuracy** among them, but considering that this model is often used to screen out monochrome images 
 and we want to screen out as many as possible without omission, we have also introduced weighted models (`safe2` and `safe4`).