Update README.md

README.md

@@ -10,12 +10,12 @@ PartImageNet++ is an extensive dataset designed for robust object recognition an
 
 
 ### Dataset Statistics
-| Obj. Cat. | Part Cat. | Img    | Part Mask |
+| Object Category | Part Category | Image    | Part Mask |
 | --------- | --------- | ------ | --------- |
 | 1000      | 3308      | 100000 | 406364    |
 
 The dataset includes:
-- **1000 object categories** derived from the original ImageNet.
+- **1000 object categories** derived from the original ImageNet-1K.
 - **3308 part categories** representing different parts of objects.
 - **100,000 annotated images**, with each object category containing 100 images.
 - **406,364 part mask annotations** ensuring comprehensive coverage and detailed segmentation.
@@ -26,17 +26,17 @@ Each JSON file in the `json` directory represents one object category and its co
 
 The `including` folder provides detailed inclusion relations of parts, illustrating hierarchical relationships between different part categories.
 
-The `discarded_data.json` file lists low-quality images that were excluded from the dataset to maintain high annotation standards.
+The `discarded_data.json` file lists low-quality images excluded from the dataset to maintain high annotation standards.
 
 ### Visualizations
 
-We provide a visualization demo tool to explore and inspect the annotations. This tool helps users to better understand the structure and details of the dataset.
+We provide a visualization demo tool to explore and inspect the annotations. This tool helps users better understand the dataset's structure and details.
 
 ### If you find this useful in your research, please cite this work:
 ```
 @inproceedings{li2024languagedriven,
   author = {Li, Xiao and Liu, Yining and Dong, Na and Qin, Sitian and Hu, Xiaolin},
-  title = PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition
+  title = {PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition},
   booktitle={European conference on computer vision},
   year = {2024},
   organization={Springer}