Update README.md

README.md

@@ -8,10 +8,9 @@ viewer: false
 
 <!-- Provide a quick summary of the dataset. -->
 
-The TopOpt Datasets Collection comprises several datasets utilized to evaluate the performance and generalization capabilities of machine learning models specifically tailored for topology optimization (TO). This collection is designed to test models across various TO methods and structural complexities, from optimized structures to random assemblies and out-of-distribution (OOD) samples.
-
+This dataset contains the training and testing sets for the YOLOv8-TO paper.
 ## Dataset Details
-
+Test
 ### Dataset Description
 
 - **Created by:** Thomas Rochefort-Beaudoin
@@ -43,18 +42,7 @@ Usage: Testing
 
 - **Repository:** https://github.com/COSIM-Lab/YOLOv8-TO
 - **Paper:** https://arxiv.org/pdf/2404.18763
-- **Demo:** [WORK IN PROGRESS]
-
-## Uses
-
-<!-- Address questions around how the dataset is intended to be used. -->
-
-### Direct Use
-
-<!-- This section describes suitable use cases for the dataset. -->
-
-[More Information Needed]
-
+- **Demo:** https://huggingface.co/spaces/tomrb/YOLOv8-TO
 
 ## Dataset Structure
 
@@ -62,26 +50,8 @@ Usage: Testing
 
 [More Information Needed]
 
-## Dataset Creation
-
-### Source Data
-
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-
-#### Data Collection and Processing
-
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
-
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
 
-## Citation [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->