Update README.md

README.md

@@ -157,7 +157,7 @@ images[0].save(f"your image save path, png format is usually better than jpg or
 
 
 ## Evaluation Metric
-1 Laion Aesthetic Score [https://laion.ai/blog/laion-aesthetics/] 
+1 Laion Aesthetic Score [https://laion.ai/blog/laion-aesthetics/]  
 2 PerceptualSimilarity [https://github.com/richzhang/PerceptualSimilarity]
 
 
@@ -167,7 +167,14 @@ and the upscale image tend to have more beauty score and prompt consistency, it
 totally 1200 images generated. We caculate the Laion Aesthetic Score to measure the beauty and the PerceptualSimilarity to measure the control ability, we find the quality of images have a good consistency with the meric values.
 We compare our methods with other SOTA huggingface models and list the result below. We are the models that have highest aesthectic score, and can generate visually appealing images if you prompt it properly.
 
+## Quantitative Result
+| metric | xinsir/controlnet-canny-sdxl-1.0 | diffusers/controlnet-canny-sdxl-1.0 | TheMistoAI/MistoLine |
+|-------|-------|-------|-------|
+| laion_aesthetic | **6.03** | 5.93 | 5.82 |
+| perceptual similarity | 0.830 | 0.844 | **0.812** |
 
+laion_aesthetic(the higher the better)  
+perceptual similarity(the lower the better)
 
 
 ## Training Details
@@ -185,7 +192,7 @@ We use over 10000000 images, which are annotated carefully, cogvlm is proved to
 The data consists of many sources, including midjourney, laion 5B, danbooru, and so on. The data is carefully filtered and annotated. 
 
 
-### Evaluation
+### Conclusion
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->