Update README.md

README.md

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 # Brain Hemorrhage Segmentation Dataset (BHSD)
 
 ## Description
-The Brain Hemorrhage Segmentation Dataset (BHSD) is a 3D multi-class segmentation dataset for intracranial hemorrhage (ICH). Intracranial hemorrhage is a pathological condition characterized by bleeding within the skull or brain, which can arise from various factors. Accurately identifying, localizing, and quantifying ICH is crucial for clinical diagnosis and treatment. Our dataset comprises 192 volumes with pixel-level annotations and unlabeled 2000 volumes across five ICH categories.
+The Brain Hemorrhage Segmentation Dataset (BHSD) is a 3D multi-class segmentation dataset for intracranial hemorrhage (ICH). Intracranial hemorrhage is a pathological condition characterized by bleeding within the skull or brain, which can arise from various factors. Accurately identifying, localizing, and quantifying ICH is crucial for clinical diagnosis and treatment. Our dataset comprises 192 volumes with pixel-level annotations and unlabeled 1980 volumes across five ICH categories.
 
 ## Data Contents
 This dataset includes the following two compressed files:
 - **label_192.zip**: Contains 192 volumes with pixel-level annotations (Files need to be suffixed nii.gz).
 - You can directly download it: wget https://huggingface.co/datasets/WuBiao/BHSD/resolve/main/label_192.zip
-- **unlabel_2000.zip**: Contains 2000 volumes of unannotated reconstructed data.
-- You can directly download it: wget https://huggingface.co/datasets/WuBiao/BHSD/resolve/main/unlabel_2000.zip
+- **unlabel_1980.zip**: Contains 1980 volumes of unannotated reconstructed data.
+- You can directly download it: wget https://huggingface.co/datasets/WuBiao/BHSD/resolve/main/unlabel_1980.zip
 
 ## Applications
 This dataset is primarily intended to support the use of deep learning techniques in medical image segmentation tasks, particularly for multi-class segmentation of intracranial hemorrhages. It can be used for supervised and semi-supervised ICH segmentation tasks, and we provide experimental results with state-of-the-art models as reference benchmarks.