---
task_categories:
  - image-to-text
task_ids:
  - image-captioning
language:
  - en
tags:
  - floorplan
  - vlm
  - vectorization
  - structured-output
  - architecture
  - vision-language
  - unsloth
size_categories:
  - 1K<n<10K
license: cc-by-4.0
datasets:
  - name: CubiCasa5K
    url: https://github.com/CubiCasa/CubiCasa5k
    description: >-
      Original floorplan image dataset with 5000 annotated floor plans.
      Annotations are SVG polygons covering 80+ floorplan object categories.
library_name: datasets
pretty_name: FloorplanVLM SFT Dataset (Flat)
---

# FloorplanVLM SFT Dataset (Flat)

A flattened HuggingFace dataset optimized for **Unsloth Studio** and other tools requiring flat column formats for VLM fine-tuning.

This is the flat version of [BinniesHK-AI/floorplan-vlm-sft-dataset](https://huggingface.co/datasets/BinniesHK-AI/floorplan-vlm-sft-dataset), which uses a chat-based messages format.

## Dataset Description

- **Task**: Image-to-text (floor plan → structured JSON)
- **Source**: CubiCasa5K (5000 real floor plans from Finnish architectural drawings)
- **Conversion**: SVG polygon annotations → structured JSON with walls, doors, windows, and rooms
- **Format**: Flat columns (image, instruction, json_string) — ready for Unsloth Studio
- **Splits**: Train (4142), Validation (397), Test (394)

## Dataset Structure

| Column | Type | Description |
|---|---|---|
| `image` | PIL.Image | Floor plan image (`F1_scaled.png` from CubiCasa5K) |
| `instruction` | `str` | Combined system + user prompt (single text field for training) |
| `json_string` | `str` | Raw JSON annotation of the floor plan |

### Example

```python
from datasets import load_dataset

ds = load_dataset("BinniesHK-AI/floorplan-vlm-sft-dataset-flat", split="train")
sample = ds[0]

print(sample["instruction"][:200])
# "You are a floor plan analysis assistant. Convert the floor plan image to structured JSON.\n\nAnalyze the provided floor plan image and output a JSON object..."

print(sample["json_string"][:200])
# '{"rooms": [{"type": "living_room", "area": 25.5, "points": [[100, 200], ...]}, ...]}'
```

## Usage with Unsloth Studio

1. Go to [Unsloth Studio](https://unsloth.ai/)
2. Select **Upload Dataset** → **HuggingFace**
3. Enter: `BinniesHK-AI/floorplan-vlm-sft-dataset-flat`
4. Map columns:
   - **Image**: `image`
   - **Instruction**: `instruction`
   - **Output**: `json_string`

## Usage with TRL SFTTrainer

```python
from datasets import load_dataset
from trl import SFTTrainer, SFTConfig

ds = load_dataset("BinniesHK-AI/floorplan-vlm-sft-dataset-flat", split="train")

def formatting_func(examples):
    """Wrap instruction + json_string into chat messages."""
    texts = []
    for inst, js in zip(examples["instruction"], examples["json_string"]):
        texts.append([
            {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": inst}]},
            {"role": "assistant", "content": [{"type": "text", "text": js}]},
        ])
    return texts

trainer = SFTTrainer(
    model=model,
    train_dataset=ds,
    args=SFTConfig(output_dir="./output", max_seq_length=4096),
    formatting_func=formatting_func,
)
trainer.train()
```

## Instruction Format

The `instruction` column contains the combined system and user prompt:

```
You are a floor plan analysis assistant. Convert the floor plan image to structured JSON.

Analyze the provided floor plan image and output a JSON object with the following structure:
{
  "rooms": [{"type": str, "area": float, "points": [[x, y], ...]}],
  "walls": [{"points": [[x1, y1], [x2, y2]], "width": float}],
  "doors": [{"position": [x, y], "width": float, "rotation": float}],
  "windows": [{"position": [x, y], "width": float, "rotation": float}]
}

Focus on accurately identifying room types, boundaries, and structural elements.
```

## JSON Annotation Format

The `json_string` column contains the raw JSON output:

```json
{
  "rooms": [
    {
      "type": "living_room",
      "area": 25.5,
      "points": [[100, 200], [300, 200], [300, 400], [100, 400]]
    },
    {
      "type": "bedroom",
      "area": 15.2,
      "points": [[300, 200], [500, 200], [500, 400], [300, 400]]
    }
  ],
  "walls": [
    {"points": [[100, 200], [500, 200]], "width": 0.2}
  ],
  "doors": [
    {"position": [200, 300], "width": 0.9, "rotation": 0}
  ],
  "windows": [
    {"position": [400, 200], "width": 1.2, "rotation": 0}
  ]
}
```

## Room Types

The dataset includes 80+ floor plan object categories from CubiCasa5K, including:

- **Rooms**: living_room, bedroom, kitchen, bathroom, hallway, etc.
- **Structures**: wall, door, window, stairs, elevator
- **Fixtures**: sink, toilet, bathtub, shower, stove, oven
- **Furniture**: bed, sofa, table, chair, desk, cabinet

## Data Splits

| Split | Samples | Description |
|---|---|---|
| train | 4,142 | Training set |
| validation | 397 | Validation set |
| test | 394 | Test set |

**Note**: 67 samples were filtered out due to SVG parse failures or oversized JSON annotations (>10KB).

## Citation

```bibtex
@inproceedings{Mai2019CubiCasa,
  title={CubiCasa5K: A Dataset and an Improved Model for Floorplan Recognition},
  author={Mai, Liu and K{\"o}pf, Bernhard and Ballas, Nicolas and Ke, Qi},
  booktitle={International Conference on Document Analysis and Recognition (ICDAR)},
  year={2019}
}
```

## License

Creative Commons Attribution 4.0 International (CC BY 4.0)