Improve dataset card: add task categories, code link, and usage instructions

Hi, I'm Niels, part of the community science team at Hugging Face.

This pull request improves the dataset card for PartImageNet++ by:
- Adding `image-classification` and `image-segmentation` to the `task_categories` in the YAML metadata.
- Adding links to the research paper and the official GitHub repository.
- Adding a "Usage" section with training and evaluation commands provided in the GitHub README.

These changes help make the dataset more discoverable and easier to use for the community.

README.md

@@ -1,14 +1,20 @@
 ---
 license: mit
-pretty_name: PartImageNet++
-viewer: false
+task_categories:
+- image-classification
+- image-segmentation
 size_categories:
 - 100K<n<1M
+pretty_name: PartImageNet++
+viewer: false
 ---
+
 ## PartImageNet++ Dataset
 
-PartImageNet++ is an extensive dataset designed for robust object recognition and segmentation tasks. This dataset expands upon the original ImageNet dataset by providing detailed part annotations for each object category.
-Official repository for [PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition](https://huggingface.co/papers/2407.10918).
+[Paper](https://huggingface.co/papers/2601.01454) | [GitHub](https://github.com/LixiaoTHU/PartImageNetPP)
+
+PartImageNet++ (PIN++) is an extensive dataset designed for robust object recognition and segmentation tasks. This dataset expands upon the original ImageNet dataset by providing detailed part annotations for each object category.
+Official repository for [PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition](https://huggingface.co/papers/2601.01454).
 
 ### Dataset Statistics
 
@@ -18,6 +24,27 @@ The dataset includes:
 - **100,000 annotated images**, with each object category containing 100 images (downloaded from the ImageNet-1K dataset).
 - **406,364 part mask annotations** ensuring comprehensive coverage and detailed segmentation.
 
+### Usage
+
+Train a model, e.g., an MPM with ResNet-50-GELU as the backbone by AT:
+
+```bash
+python -m torch.distributed.launch --nproc_per_node=8 adv_train.py --configs=./train_configs/1k_pp/resnet50_part_pp_init.yaml
+
+python -m torch.distributed.launch --nproc_per_node=8 adv_train.py --configs=./train_configs/1k_pp/resnet50_part_pp_at.yaml
+```
+
+Evaluate the trained models with Autoattack under different attack threats, e.g., the MPM model under L2 attack with $\epsilon = 2$ :
+
+```bash
+python eval.py \
+    --configs=./train_configs/1k_pp/resnet50_part_pp_at.yaml \
+    --checkpoint=/home/user/ssd/*/checkpoint.pth \
+    --attack_types="autoattack" \
+    --norm "L2" \
+    --eval-eps 2
+```
+
 ### Structure and Contents
 
 Each JSON file in the `json` directory represents one object category and its corresponding part annotations. 
@@ -26,14 +53,15 @@ The `including` folder provides detailed inclusion relations of parts, illustrat
 
 The `discarded_data.json` file lists low-quality images excluded from the dataset to maintain high annotation standards.
 
-The category_name.json file contains each JSON file's file name, along with its corresponding part name and object name.
+The `category_name.json` file contains each JSON file's file name, along with its corresponding part name and object name.
 
 ### Visualizations
 
 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:
-```
+### Citation
+If you find this useful in your research, please cite this work:
+```bibtex
 @inproceedings{li2024pinpp,
   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},
@@ -41,4 +69,4 @@ We provide a visualization demo tool to explore and inspect the annotations. Thi
   year = {2024},
   organization={Springer}
 }
-```
+```