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README.md

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 license: mit
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+
+# HIPIE: Hierarchical Open-vocabulary Universal Image Segmentation
+
+PyTorch implementation of HIPIE from ["Hierarchical Open-vocabulary Universal Image Segmentation"](https://arxiv.org/abs/2307.00764) (Wang et al., NeurIPS 2023).
+
+## Pretrained Weights
+
+We provide ViT-H and ResNet-50 weights for hierarchical and part-aware image segmentation across multiple datasets:
+
+| Format | Filename | Description |
+|--------|----------|-------------|
+| ViT-H (O365, COCO, RefCOCO, PACO)    | `vit_h_cloud.pth`         | Pretrained with O365,COCO,RefCOCO,PACO |
+| ViT-H (COCO, RefCOCO, Pascal-Parts)  | `vit_h_cloud_parts.pth`   | Finetuned on COCO,RefCOCO,Pascal-Parts |
+| ResNet-50 (Pascal-Parts)             | `r50_parts.pth`           | Pretrained with O365,COCO,RefCOCO,Pascal Panoptic Parts |
+
+## Usage
+
+For demo notebooks, model configs, and inference scripts, see the [GitHub repository](https://github.com/berkeley-hipie/HIPIE).
+
+## Citation
+
+@inproceedings{wang2023hierarchical,
+title={Hierarchical Open-vocabulary Universal Image Segmentation},
+author={Wang, Xudong and Li, Shufan and Kallidromitis, Konstantinos and Kato, Yusuke and Kozuka, Kazuki and Darrell, Trevor},
+booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
+year={2023}
+}

r50_parts.pth

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vit_h_cloud.pth

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vit_h_cloud_parts.pth

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