Update README.md

README.md

@@ -1,3 +1,74 @@
----
-license: mit
----
+---
+language: en
+license: mit
+tags:
+  - computer-vision
+  - image-segmentation
+  - semantic-segmentation
+  - instance-segmentation
+  - segformer
+  - mask-rcnn
+  - mmsegmentation
+  - detectron2
+  - photovoltaics
+  - bipv
+  - building-facades
+library_name: pytorch
+pipeline_tag: image-segmentation
+---
+
+# BIPVfinder
+
+BIPVfinder provides deep-learning checkpoints for segmenting **building-integrated photovoltaics (BIPV)** on building façades from street-level and web imagery. The associated codebase (training + evaluation + processing) lives in the GitHub repository linked below.
+
+- Code: https://github.com/ycdrn/BIPVfinder
+- Training dataset repo: https://github.com/ycdrn/bipv_facades
+- Checkpoints in this HF repo: `model_ckpts.zip`
+
+## Model summary
+
+This Hub repository hosts fine-tuned checkpoints used in:
+
+> *Deep learning for BIPV segmentation on facades: Comparison with human annotations across facade designs* (Building and Environment, 2026).  
+DOI: https://doi.org/10.1016/j.buildenv.2026.114292
+
+Two architectures are referenced in the project:
+- **SegFormer** (semantic segmentation)
+- **Mask R-CNN** (instance segmentation)
+
+## Intended use
+
+Primary intended uses:
+- Research on façade PV / BIPV recognition and segmentation
+- Automated BIPV mask creation as an input to downstream tasks such as PV area estimation and energy-yield estimation (see the accompanying paper/code)
+
+## Training data
+
+Models were trained using the **BIPV Facades Dataset** (curated set of 400 BIPV façade projects; see dataset repository).  
+- Dataset repo: https://github.com/ycdrn/bipv_facades  
+
+## How to use
+
+From this repo, download `model_ckpts.zip` and extract it locally, e.g.:
+
+```bash
+unzip model_ckpts.zip -d model_ckpts
+```
+
+Then place the downloaded ckpts in the respective folder: [/files/03_model_checkpoints/](https://github.com/ycdrn/BIPVfinder/tree/82116e240f9e786cedf57d532110bc3066d721d0/files/03_model_checkpoints).
+Please check the [paper repository](https://github.com/ycdrn/BIPVfinder) for further details and inference.
+
+## Citation
+
+```bibtex
+@article{DURAN2026114292,
+  title   = {Deep learning for BIPV segmentation on facades: Comparison with human annotations across facade designs},
+  journal = {Building and Environment},
+  pages   = {114292},
+  year    = {2026},
+  issn    = {0360-1323},
+  doi     = {10.1016/j.buildenv.2026.114292},
+  url     = {https://www.sciencedirect.com/science/article/pii/S0360132326000983},
+  author  = {Ayca Duran and Pedram Mirabian and Panagiotis Karapiperis and Christoph Waibel and Bernd Bickel and Arno Schlueter}
+}
+```