---
license: cc-by-nc-4.0
base_model:
- Ultralytics/YOLOv8
pipeline_tag: object-detection
---

# Architect (YOLOv8m)

`Architect` is a fine-tuned YOLOv8m model for **architectural symbol spotting** in rasterized floor plans and CAD drawings. Developed as part of the `Architecture-RAG` project, it empowers multimodal systems to understand structured architectural content.

## Model Summary

- **Base Model:** YOLOv8m (pretrained on COCO)  
- **Task:** Object detection (28 architectural object categories)  
- **Dataset:** [FloorPlanCAD](https://floorplancad.github.io/)  
- **Performance:**  
  - **mAP50-95(B):** 0.80797  
  - **mAP50(B):** 0.87664  

---

## ✅ Supported Classes (28)

{
  'single door': 0, 'double door': 1, 'sliding door': 2, 'window': 3, 'bay window': 4,
  'blind window': 5, 'opening symbol': 6, 'stair': 7, 'gas stove': 8, 'refrigerator': 9,
  'washing machine': 10, 'sofa': 11, 'bed': 12, 'chair': 13, 'table': 14,
  'bedside cupboard': 15, 'TV cabinet': 16, 'half-height cabinet': 17, 'high cabinet': 18,
  'wardrobe': 19, 'sink': 20, 'bath': 21, 'bath tub': 22, 'squat toilet': 23, 'urinal': 24,
  'toilet': 25, 'elevator': 26, 'escalator': 27
}

## 🧪 How to Use


```python
from ultralytics import YOLO
from PIL import Image

# Load the model from Hugging Face Hub
model = YOLO('SamirShabani/Architect')

# Run inference on a local image file
results = model('path/to/image.png')

# Optionally, run inference on a PIL Image
# image = Image.open('path/to/image.png')
# results = model(image)[0]

# Print detection results
for r in results:
    for box in r.boxes:
        class_id = int(box.cls[0])
        class_name = model.names[class_id]
        confidence = float(box.conf[0])
        bbox = box.xyxy[0].tolist()
        print(f"Detected: {class_name}, Confidence: {confidence:.2f}, BBox: {bbox}")

# Save output image with drawn bounding boxes
results[0].save(filename="prediction_output.jpg")
```

## 🛠️ Training Details

- Framework: Ultralytics YOLOv8  
- Pretrained Model: yolov8m.pt  
- Training Hardware: NVIDIA Tesla P100 / T4 (Kaggle)  
- Epochs: 100 (early stopping patience=20)  
- Image Size: 640 × 640  
- Batch Size: 16  
- Optimizer: AdamW  
- Scheduler: Cosine Annealing  

---

## 📦 Dataset

- Source: FloorPlanCAD (https://floorplancad.github.io/)  
- Images: 15,285 SVG drawings → converted to 640×640 PNG images  
- Labeled Samples: ~11,35 images with bounding box annotations  
- License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)  
  Non-commercial use only  

---

## 📊 Evaluation Metrics (Epoch 54)

Metric               | Value    | Description
---------------------|----------|-------------------------------------------
metrics/mAP50-95(B)  | 0.80797  | Mean Average Precision [IoU = 0.50 to 0.95]
metrics/mAP50(B)     | 0.87664  | Mean Average Precision at IoU = 0.50
train/box_loss       | 0.4671   | Localization loss on training set
val/box_loss         | 0.32854  | Localization loss on validation set
train/cls_loss       | 0.81329  | Classification loss on training set
val/cls_loss         | 0.57334  | Classification loss on validation set

Training and validation curves are available in the results.png generated during training.

---

## ⚠️ Known Limitations

- Symbol Bias: Frequent objects like doors and windows dominate the training samples.  
- Centering Bias: Objects are mostly centered in cropped training patches.  
- Text Ignorance: The model does **not** interpret text or annotations near symbols.  
- "Stuff" Categories Ignored: The model does **not** detect background elements like walls or parking spaces.  
- Low-Quality Documents: Performance may degrade on scanned or low-resolution plans with noise.  

---

## 📚 Citation
```bibtex
@InProceedings{Fan_2021_ICCV,
  author    = {Fan, Zhiwen and Zhu, Lingjie and Li, Honghua and Zhu, Siyu and Tan, Ping},
  title     = {FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  month     = {October},
  year      = {2021}
}
```

## 👤 Creator

Samir Shabani  
Machine Learning Engineer | Student

LinkedIn: https://www.linkedin.com/in/samir-shabani  
GitHub: https://github.com/Sam1rShaban1