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README.md

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 These model is used for monochrome image classification, based on CNNs and Transformers, trained with dataset [deepghs/monochrome_danbooru(private)](https://huggingface.co/datasets/deepghs/monochrome_danbooru).
 
+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%     |
+
+**`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`). 
+Although their overall accuracy has been slightly reduced, the probability of False Negative (misidentifying a monochrome image as a colored one) is lower, 
+making them more suitable for batch screening.
+
+## Deepdanbooru
+
+`deepdanbooru` is a model used to tag anime images. Here, we provide a table for tag classification called `deepdanbooru_tags.csv`, 
+as well as an ONNX model (from [chinoll/deepdanbooru](https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags)).
+
+It's worth noting that due to the poor quality of the deepdanbooru model itself and the relatively old dataset, 
+it is only for testing purposes and is not recommended to be used as the main classification model. We recommend using the `wd14` model instead, see:
+
+* https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags
+