Update README.md

README.md

@@ -192,6 +192,36 @@ It is tailored for segmentation and classification tasks. It contains 3 differen
 
 All CT scans and segmentation masks had already been resampled with voxel spacing (2.0, 2.0, 3.0) and thus its reduced file size.
 
+### Usage
+
+```python
+from datasets import load_dataset
+
+# Classification dataset
+rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", streaming=True) # "classification" is the default configuration
+rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", streaming=True) # download dataset on-demand and in-memory (no caching)
+rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", streaming=False) # download dataset and cache locally (~90.09 GiB)
+rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", streaming=True, test_size=0.05, random_state=42) # specify split size for train-test split
+
+# Classification dataset with segmentation masks
+rsna_clsmask_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification-with-mask", streaming=True) # download dataset on-demand and in-memory (no caching)
+rsna_clsmask_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification-with-mask", streaming=False) # download dataset and cache locally (~3.91 GiB)
+rsna_clsmask_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification-with-mask", streaming=False, test_size=0.05, random_state=42) # specify split size for train-test split
+
+# Segmentation dataset
+rsna_seg_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "segmentation", streaming=True) # download dataset on-demand and in-memory (no caching)
+rsna_seg_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "segmentation", streaming=False) # download dataset and cache locally (~3.91 GiB)
+rsna_seg_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "segmentation", streaming=True, test_size=0.1, random_state=42) # specify split size for train-test split
+
+# Get the dataset splits
+train_rsna_cls_ds = rsna_cls_ds["train"]; test_rsna_cls_ds = rsna_cls_ds["test"]
+train_rsna_clsmask_ds = rsna_clsmask_ds["train"]; test_rsna_clsmask_ds = rsna_clsmask_ds["test"]
+train_rsna_seg_ds = rsna_seg_ds["train"]; test_rsna_seg_ds = rsna_seg_ds["test"]
+
+# Tip: Download speed up with multiprocessing
+rsna_cls_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", streaming=False, num_proc=8) # num_proc: num of cpu core used for loading the dataset
+```
+
 ## Dataset Structure
 
 ### Data Instances
@@ -308,6 +338,9 @@ Modify the split proportion:
 rsna_ds = load_dataset("jherng/rsna-2023-abdominal-trauma-detection", "classification", test_size=0.05, random_state=42)
 ```
 
+
+## Additional Information
+
 ### Citation Information
 
 - Preprocessed dataset: