Update README.md

README.md

@@ -25,6 +25,7 @@ The dataset is organized into the following folders:
   - `jsons3`: Contains the JSON files corresponding to the images in the `images3` folder.
 
 - **QA Files**
+These are the QA created in our proposed dataset. 
   - `qa`: Contains the QA files corresponding to the images in the `images` folder.
   - `qa2`: Contains the QA files corresponding to the images in the `images2` folder.
   - `qa3`: Contains the QA files corresponding to the images in the `images3` folder.
@@ -32,11 +33,12 @@ The dataset is organized into the following folders:
 ### File Details
 
 - **Images**: JPEG files named in the format `PMCxxxxxx_abc.jpg`, where `xxxxxx` represents the PubMed Central ID and `abc` represents an identifier specific to the image.
-- **JSON Files**: JSON files named in the same format as the images. Each JSON file is a list of question blocks associated with the corresponding image. 
+- **JSON Files**: JSON files named in the same format as the images. These are groundtruth annotations from the https://chartinfo.github.io challenge, they provide annotations for chart type, text(OCR), text location, text type (axis/tick/legend), data used to plot the chart. 
+- **QA Files**: QA files named in the same format as the images. Each QA  file is a list of question blocks associated with the corresponding image we created in our proposed dataset. 
 
-#### JSON Structure
+#### QA Structure
 
-Each JSON file contains a list of question blocks in the following format:
+Each QA file contains a list of question blocks in the following format:
 
 ```json
 [
@@ -61,8 +63,6 @@ Each JSON file contains a list of question blocks in the following format:
 ]
 ```
 
-- **QA Files**: Contain additional question-answer metadata relevant to the dataset.
-
 ### Dataset Loader
 
 To facilitate loading and using the dataset, we provide a custom dataset loader script, `dataset.py`. This script defines a PyTorch `Dataset` class to handle loading, preprocessing, and batching of the images and question-answer pairs.
@@ -85,22 +85,20 @@ To facilitate loading and using the dataset, we provide a custom dataset loader
    from dataset import RQADataset
    from torch.utils.data import DataLoader
 
-   # Define the data configuration
-   class DataConfig:
-       img_dir = '/home/jupyter/data/RQA/images'  # Update with actual image directory path
-       json_dir = '/home/jupyter/data/RQA/jsons'  # Update with actual JSON directory path
-       filter_list = '/home/jupyter/data/RQA_V0/test_filenames.txt'  # Path to the file containing test filenames
-       train = False  # Set to True for training, False for testing
+   dataset = RQADataset(data_dir='.', split='train') # split='test' for RQA9357 split used in the paper
 
-   # Initialize dataset
-   dataset = RQADataset(DataConfig)
+    # Test loading a single item
+    print(f"Number of samples in dataset: {len(dataset)}")
+    sample = dataset[0]
+    print("Sample data:", sample)
 
-   # Initialize DataLoader
-   dataloader = DataLoader(dataset, batch_size=4, collate_fn=RQADataset.custom_collate)
+    # Initialize DataLoader
+    dataloader = DataLoader(dataset, batch_size=4, collate_fn=RQADataset.custom_collate)
 
-   # Iterate through the dataset
-   for batch in dataloader:
-       print(batch)
+    # Test DataLoader
+    for batch in dataloader:
+        print("Batch data:", batch)
+        break  # Load only one batch for testing
    ```
 
 ### Citation