Marco333
/

segformer-b0-facade-cmp

@@ -1,199 +1,111 @@
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+# SegFormer-B0 Fine-Tuned on CMP Facade Dataset
+Custom semantic segmentation model for facade parsing: wall, window, door, and balcony detection on rectified building facades.
 ## Model Details
+- **Architecture**: SegFormer-B0 (NVIDIA, ADE20K-pretrained)
+- **Parameters**: ~3.7M
+- **Task**: Semantic Segmentation
+- **Input Size**: 512×512
+- **Classes**: 6 unified facade classes
+## Class Mapping
+| ID | Class | Description |
+|----|-------|-------------|
+| 0 | `background` | Sky, ground, non-facade regions |
+| 1 | `facade_wall` | Main wall surface + moldings, cornices, pillars, sills, deco |
+| 2 | `window` | Windows + blinds |
+| 3 | `door` | Doors + shopfronts |
+| 4 | `balcony` | Balconies |
+| 5 | `vegetation_occluder` | Vegetation (trained as background since CMP lacks this class) |
+## Training
+- **Dataset**: [CMP Facade Database](https://huggingface.co/datasets/Xpitfire/cmp_facade) — 378 train, 114 test rectified facade images
+- **Original Classes**: 12 (facade, molding, cornice, pillar, window, door, sill, blind, balcony, shop, deco, background)
+- **Mapping**: 12 CMP classes → 6 unified classes (see mapping above)
+- **Epochs**: ~53 (best at epoch 38, mean IoU 0.4856)
+- **Optimizer**: AdamW, lr=6e-5
+- **Batch Size**: 4 per device (effective batch = 8 with grad accumulation)
+- **Hardware**: Tesla T4 GPU
+## Best Validation Metrics
+| Metric | Value |
+|--------|-------|
+| Mean IoU | 0.4856 |
+| Facade Wall IoU | 0.867 |
+| Window IoU | 0.410 |
+| Door IoU | 0.460 |
+| Balcony IoU | 0.230 |
+| Background IoU | 0.467 |
+## Usage
+```python
+from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
+from PIL import Image
+import torch.nn as nn
+import torch
+# Load model
+processor = SegformerImageProcessor.from_pretrained("Marco333/segformer-b0-facade-cmp")
+model = SegformerForSemanticSegmentation.from_pretrained("Marco333/segformer-b0-facade-cmp")
+# Load image
+image = Image.open("facade.jpg").convert("RGB")
+# Inference
+inputs = processor(images=image, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+# Upsample to original size
+upsampled = nn.functional.interpolate(
+    logits, size=image.size[::-1], mode="bilinear", align_corners=False
+)
+pred_seg = upsampled.argmax(dim=1)[0].cpu().numpy()
+```
+## Intended Use
+- **Primary**: Second-pass segmentation of rectified facades (after homography rectification)
+- **Secondary**: First-pass facade detection on raw street photos (with expected lower accuracy due to lack of unrectified training data)
+## Pipeline Role
+This model is designed for use in a 2-pass facade segmentation pipeline:
+1. Pass 1: Segment raw street photo → find facade wall region
+2. Rectify facade via homography
+3. Pass 2: Re-run this model on rectified crop → parse windows, doors, balconies cleanly
+## Limitations
+- Trained only on **rectified** facade images from CMP. Performance on perspective-distorted street photos will be degraded.
+- No vegetation data in training set — `vegetation_occluder` class will detect as background.
+- Small dataset (378 images) — performance ceiling is moderate.
+## Citation
+CMP Dataset:
+```bibtex
+@INPROCEEDINGS{Tylecek13,
+  author = {Radim Tyle{\v c}ek and Radim {\v S}{\' a}ra},
+  title = {Spatial Pattern Templates for Recognition of Objects with Regular Structure},
+  booktitle = {Proc. GCPR},
+  year = {2013},
+}
+```
+SegFormer:
+```bibtex
+@article{xie2021segformer,
+  title={SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers},
+  author={Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M and Luo, Ping},
+  journal={arXiv preprint arXiv:2105.15203},
+  year={2021}
+}
+```