Update README.md

README.md

@@ -97,14 +97,12 @@ Llama-3-8B: 8B parameters
 ## Evaluation
 
 
-### Testing Data, Factors & Metrics
-
-#### Testing Data
+### Testing Data
 
 The testing data is from an open-source dataset, DeepCAD: https://github.com/ChrisWu1997/DeepCAD?tab=readme-ov-file. We use its pre-processed version:  https://github.com/samxuxiang/SkexGen.  
 
 
-#### Metrics
+### Metrics
 
 - Generation diversity and quality on the generated CAD models in comparison to the test set, including Coverage (COV), Minimum Matching Distance (MMD) and Jensen-Shannon Divergence (JSD) [\[1\]](https://arxiv.org/abs/2207.04632)[\[2\]](https://arxiv.org/abs/2307.00149). 
 - The percentage of predicted CAD sequences that can be successfully rendered into 3D models, denoted as Prediction Validity (PV). 
@@ -112,7 +110,6 @@ The testing data is from an open-source dataset, DeepCAD: https://github.com/Chr
 
 ### Results
 
-#### Summary
 We use prior work, including [Skexgen](https://arxiv.org/abs/2207.04632), [Hnc-cad](https://arxiv.org/abs/2307.00149), and prompting [GPT-4o](https://platform.openai.com/docs/models#gpt-4o), as baselines. FlexCAD demonstrates superior performance compared to these baselines across most metrics. Notably, it achieves significant improvements in PV, with the PV values for GPT-4o, Skexgen, Hnc-cad, and FlexCAD being 62.3%, 68.7%, 72.6%, and 93.4% respectively, in the context of sketch-level controllable generation. See Table 1 for the complete evaluation in our paper (https://arxiv.org/pdf/2411.05823). 
 
 
@@ -123,8 +120,6 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Citation
 
-**BibTeX:**
-
 ```
 @InProceedings{zhang2024flexcad,
   title={FlexCAD: Unified and Versatile Controllable CAD Generation with Fine-tuned Large Language Models},