Updated Readme

README.md

@@ -42,31 +42,49 @@ tags:
 This is the dataset for the [ICDAR 2025 Competition on Comics Understanding in the Era of Foundational Models](https://rrc.cvc.uab.es/?ch=31&com=introduction)
 
 The dataset contains five subtask or skills:
-- Sequence Filling
-- Character Coherence
-- Visual Closure
-- Text Closure
-- Caption Relevance
 
-## Skills:
 ### Sequence Filling
+<details>
+<summary>Task Description</summary>
 ![Sequence Filling](figures/seq_filling.png)
 Given a sequence of comic panels, a missing panel, and a set of option panels, the task is to select the panel that best fits the sequence.
+</details>
 
 ### Character Coherence, Visual Closure, Text Closure
+<details>
+<summary>Task Description</summary>
 ![Character Coherence](figures/closure.png)
 These skills require understanding the context sequence to then pick the best panel to continue the story, focusing on the characters, the visual elements, and the text:
 - Character Coherence: Given a sequence of comic panels, pick the panel from the two options that best continues the story in a coherent with the characters. Both options are the same panel, but the text in the speech bubbles is has been swapped.
 - Visual Closure: Given a sequence of comic panels, pick the panel from the options that best continues the story in a coherent way with the visual elements.
 - Text Closure: Given a sequence of comic panels, pick the panel from the options that best continues the story in a coherent way with the text. All options are the same panel, but with text in the speech retrieved from different panels.
+</details>
 
 ### Caption Relevance
+<details>
+<summary>Task Description</summary>
 ![Caption Relevance](figures/caption_relevance.png)
 Given a caption from the previous panel, select the panel that best continues the story.
+</details>
 
 ## Loading the Data
+
+```python
+from datasets import load_dataset
+
+skill = "seq_filling" # "seq_filling", "char_coherence", "visual_closure", "text_closure", "caption_relevance"
+split = "val" # "test"
+dataset = load_dataset("VLR-CVC/ComPAP", skill, split=split)
+```
+
+<details>
+<summary>Map to single images</summary>
+If your model can only process single images, you can render each sample as a single image:
+
 _coming soon_
 
+</details>
+
 ## Summit Results and Leaderboard
 The competition is hosted in the [Robust Reading Competition website](https://rrc.cvc.uab.es/?ch=31&com=introduction) and the leaderboard is available [here](https://rrc.cvc.uab.es/?ch=31&com=evaluation).