Update README.md

fix typos and polish TAIX-Ray dataset README

README.md

@@ -127,36 +127,47 @@ dataset_info:
   dataset_size: 1226435862593.624
 ---
 
+
 # TAIX-Ray Dataset
 
-TAIX-Ray is a comprehensive dataset of about 200k bedside chest radiographs from about 50k intensive care patients at the University Hospital in Aachen, Germany, collected between 2010 and 2024. 
-Trained radiologists provided structured reports at the time of acquisition, assessing key findings such as cardiomegaly, pulmonary congestion, pleural effusion, pulmonary opacities, and atelectasis on an ordinal scale. 
+TAIX-Ray is a comprehensive dataset of approximately 200k bedside chest radiographs from around 50k intensive care patients at University Hospital Aachen, Germany, collected between 2010 and 2024.
 
+Trained radiologists provided structured reports at the time of acquisition, assessing key findings such as cardiomegaly, pulmonary congestion, pleural effusion, pulmonary opacities, and atelectasis on an ordinal scale.
 
 <br>
 
-## Code & Details:
-The code for data loading, preprocessing, and baseline experiments is available at:  https://github.com/mueller-franzes/TAIX-Ray
+## Code & Details
+
+The code for data loading, preprocessing, and baseline experiments is available at:
+[https://github.com/mueller-franzes/TAIX-Ray](https://github.com/mueller-franzes/TAIX-Ray)
+
+---
 
 ## How to Use
 
 ### Prerequisites
+
 Ensure you have the following dependencies installed:
 
 ```bash
 pip install datasets matplotlib huggingface_hub pandas tqdm
 ```
 
-### Configurations
-This dataset is available in two configurations. 
+---
+
+## Configurations
+
+This dataset is available in two configurations:
 
 | **Name** | **Size** | **Image Size** |
-|------------|----------|----------------|
-| default    | 62GB     | 512px          |
-| original   | 1.2TB    | variable       |
+| -------- | -------- | -------------- |
+| default  | 62GB     | 512px          |
+| original | 1.2TB    | variable       |
 
+---
+
+## Option A: Use within the Hugging Face Framework
 
-### Option A: Use within the Hugging Face Framework
 If you want to use the dataset directly within the Hugging Face `datasets` library, you can load and visualize it as follows:
 
 ```python
@@ -167,26 +178,29 @@ from matplotlib import pyplot as plt
 dataset = load_dataset("TLAIM/TAIX-Ray", name="default")
 
 # Access the training split (Fold 0)
-ds_train = dataset['train']
+ds_train = dataset["train"]
 
 # Retrieve a single sample from the training set
 item = ds_train[0]
 
 # Extract and display the image
-image = item['Image']
-plt.imshow(image, cmap='gray')
-plt.savefig('image.png')  # Save the image to a file
+image = item["Image"]
+plt.imshow(image, cmap="gray")
+plt.savefig("image.png")  # Save the image to a file
 plt.show()  # Display the image
 
 # Print metadata (excluding the image itself)
 for key in item.keys():
-    if key != 'Image':
+    if key != "Image":
         print(f"{key}: {item[key]}")
 ```
 
-### Option B: Downloading the Dataset 
+---
+
+## Option B: Downloading the Dataset
 
 If you prefer to download the dataset to a specific folder, use the following script. This will create the following folder structure:
+
 ```
 .
 ├── data/
@@ -194,13 +208,12 @@ If you prefer to download the dataset to a specific folder, use the following sc
 │   ├── d8546c6108aad271211da996eb7e9eeabaf44d39cf0226a4301c3cbe12d84151.png
 │   └── ...
 └── metadata/
-    ├── annoation.csv
-    └── split.csv 
+    ├── annotation.csv
+    └── split.csv
 ```
 
 ```python
 from datasets import load_dataset
-from huggingface_hub import hf_hub_download
 from pathlib import Path
 import pandas as pd
 from tqdm import tqdm
@@ -208,19 +221,19 @@ from tqdm import tqdm
 # Define output paths
 output_root = Path("./TAIX-Ray")
 
-# Create folders 
+# Create folders
 data_dir = output_root / "data"
 metadata_dir = output_root / "metadata"
 data_dir.mkdir(parents=True, exist_ok=True)
 metadata_dir.mkdir(parents=True, exist_ok=True)
 
 # Load dataset in streaming mode
-dataset = dataset = load_dataset("TLAIM/TAIX-Ray", name="default",  streaming=True)
+dataset = load_dataset("TLAIM/TAIX-Ray", name="default", streaming=True)
 
 # Process dataset
 metadata = []
 for split, split_dataset in dataset.items():
-    print("-------- Start Download: ", split, " --------")
+    print("-------- Start Download:", split, "--------")
     for item in tqdm(split_dataset, desc="Downloading"):  # Stream data one-by-one
         uid = item["UID"]
         img = item.pop("Image")  # PIL Image object
@@ -229,16 +242,19 @@ for split, split_dataset in dataset.items():
         img.save(data_dir / f"{uid}.png", format="PNG")
 
         # Store metadata
-        metadata.append(item)  
+        metadata.append(item)
 
 # Convert metadata to DataFrame
 metadata_df = pd.DataFrame(metadata)
 
-# Save annotations to CSV files
-metadata_df.drop(columns=["Split", "Fold"]).to_csv(metadata_dir / "annotation.csv", index=False)
-
+# Save annotations to CSV file
+metadata_df.drop(columns=["Split", "Fold"]).to_csv(
+    metadata_dir / "annotation.csv", index=False
+)
 
 print("Dataset streamed and saved successfully!")
 ```
 
 
+
+