Update README.md

README.md

@@ -28,44 +28,61 @@ size_categories:
 
 ### Dataset Summary
 
-This diagnostic dataset is specifically designed to evaluate the visual logical learning capabilities of machine learning models.
+This diagnostic dataset ([website](https://sites.google.com/view/v-lol), [paper](https://doi.org/10.48550/arXiv.2306.07743)) is specifically designed to evaluate the visual logical learning capabilities of machine learning models.
 It offers a seamless integration of visual and logical challenges, providing 2D images of complex visual trains,
 where the classification is derived from rule-based logic.
 The fundamental idea of V-LoL remains to integrate the explicit logical learning tasks of classic symbolic AI benchmarks into visually complex scenes,
 creating a unique visual input that retains the challenges and versatility of explicit logic.
 In doing so, V-LoL bridges the gap between symbolic AI challenges and contemporary deep learning datasets offering various visual logical learning tasks
 that pose challenges for AI models across a wide spectrum of AI research, from symbolic to neural and neuro-symbolic AI.
-Moreover, we provide a flexible [dataset generator](https://github.com/ml-research/vlol-dataset-gen) that
+Moreover, we provide a flexible dataset generator ([GitHub](https://github.com/ml-research/vlol-dataset-gen)) that
 empowers researchers to easily exchange or modify the logical rules, thereby enabling the creation of new datasets incorperating novel logical learning challenges.
 By combining visual input with logical reasoning, this dataset serves as a comprehensive benchmark for assessing the ability
 of machine learning models to learn and apply logical reasoning within a visual context.
 
 ### Supported Tasks and Leaderboards
 
-We offer a diverse set of datasets that present challenging AI tasks targeting various reasoning abilities. The following provides an overview of the available datasets:
+We offer a diverse set of datasets that present challenging AI tasks targeting various reasoning abilities.
+The following provides an overview of the available V-LoL challenges and corresponding dataset splits.
+
+| V-LoL Challenges            | Train set | Validation set  |  # of train samples |  # of validation samples |
+| ---         | ---   | -----------         | ---        |  -----------             |
+| V-LoL-Trains-TheoryX | V-LoL-Trains-TheoryX | V-LoL-Trains-TheoryX |  10000 | 2000  |
+| V-LoL-Trains-Numerical | V-LoL-Trains-Numerical | V-LoL-Trains-Numerical |  10000 | 2000  |
+| V-LoL-Trains-Complex   | V-LoL-Trains-Complex | V-LoL-Trains-Complex |  10000 | 2000  |
+| V-LoL-Blocks-TheoryX   | V-LoL-Blocks-TheoryX | V-LoL-Blocks-TheoryX |  10000 | 2000  |
+| V-LoL-Blocks-Numerical | V-LoL-Blocks-Numerical | V-LoL-Blocks-Numerical |  10000 | 2000  |
+| V-LoL-Blocks-Complex   | V-LoL-Blocks-Complex | V-LoL-Blocks-Complex |  10000 | 2000  |
+| V-LoL-Trains-TheoryX-len7   | V-LoL-Trains-TheoryX | V-LoL-Trains-TheoryX-len7 |  12000 | 2000  |
+| V-LoL-Trains-Numerical-len7 | V-LoL-Trains-Numerical | V-LoL-Trains-Numerical-len7 |  12000 | 2000  |
+| V-LoL-Trains-Complex-len7   | V-LoL-Trains-Complex | V-LoL-Trains-Complex-len7 |  12000 | 2000  |
+| V-LoL-Random-Trains-TheoryX | V-LoL-Trains-TheoryX | V-LoL-Random-Trains-TheoryX |  12000 | 12000  |
+| V-LoL-Random-Blocks-TheoryX | V-LoL-Blocks-TheoryX | V-LoL-Random-Blocks-TheoryX |  12000 | 12000  |
+
+The following gives more detailed explanations of the different V-LoL challenges:
 
 Logical complexity:
 
-- Theory X: The train has either a short, closed car or a car with a barrel load is somewhere behind a car with a golden vase load. This rule was originally introduced as "Theory X" in the new East-West Challenge.
+- Theory X (marked 'TheoryX'): The train has either a short, closed car or a car with a barrel load is somewhere behind a car with a golden vase load. This rule was originally introduced as "Theory X" in the new East-West Challenge.
 
-- Numerical rule: The train has a car where its car position equals its number of payloads which equals its number of wheel axles.
+- Numerical rule (marked 'Numerical'): The train has a car where its car position equals its number of payloads which equals its number of wheel axles.
 
-- Complex rule: Either, there is a car with a car number which is smaller than its number of wheel axles count and smaller than the number of loads, or there is a short and a long car with the same colour where the position number of the short car is smaller than the number of wheel axles of the long car, or the train has three differently coloured cars. We refer to Tab. 3 in the supp. for more insights on required reasoning properties for each rule.
+- Complex rule (marked 'Complex'): Either, there is a car with a car number which is smaller than its number of wheel axles count and smaller than the number of loads, or there is a short and a long car with the same colour where the position number of the short car is smaller than the number of wheel axles of the long car, or the train has three differently coloured cars. We refer to Tab. 3 in the supp. for more insights on required reasoning properties for each rule.
 
 Visual complexity:
 
-- Realistic train representaions.
-- Block representation.
+- Realistic train representaions. (marked 'Trains')
+- Block representation. (marked 'Blocks')
 
 OOD Trains:
 
-- A train carrying 2-4 cars.
-- A train carrying 7 cars.
+- A train carrying 2-4 cars. (default)
+- A train carrying 7 cars.  (marked 'len7')
 
 Train attribute distributions:
 
-- Michalski attribute distribution.
-- Random attribute distribution.
+- Michalski attribute distribution.  (default)
+- Random attribute distribution.  (marked 'Random')
 
 ### Languages
 
@@ -102,9 +119,9 @@ Class labels mapping:
 ### Data Splits
 
 
-|             | Train | Validation |
-| ---         | --- | ----------- |
-| # of samples | 10000 | 2000 |
+See tasks.
+
+
 
 ## Dataset Creation