README.md

---
license: cc-by-4.0
task_categories:
- text-to-3d
- image-to-3d
language:
- en
tags:
- CAD
- code-generation
---
# CADFS Dataset

<div align="center">

<p style="font-size:36px;">CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models</p>
<div style="display: flex; justify-content: center; gap: 10px;">
  <a href="https://voyleg.github.io/cadfs/" style="display:inline-block;background:#212121;color:white;padding:6px 18px;border-radius:999px;text-decoration:none;font-family:'Segoe UI','Helvetica Neue',Arial,sans-serif;">🚀 Project Page</a>
  <a href="https://github.com/VladPyatov/CADFS" style="display:inline-block;background:#212121;color:white;padding:6px 18px;border-radius:999px;text-decoration:none;font-family:'Segoe UI','Helvetica Neue',Arial,sans-serif;">💻 Code</a>
  <a href="https://huggingface.co/VladPyatov/CADFS-2B" style="display:inline-block;background:#212121;color:white;padding:6px 18px;border-radius:999px;text-decoration:none;font-family:'Segoe UI','Helvetica Neue',Arial,sans-serif;">🤗 Model</a>
</div>

</div>

<br>

A large-scale dataset for **parametric CAD model generation** from text descriptions and multi-view images. Models are represented as [FeatureScript](https://cad.onshape.com/FsDoc/) programs, enabling direct import into Onshape environment.

This dataset was used to train and evaluate **[CADFS](https://huggingface.co/VladPyatov/CADFS-2B)**, a fine-tuned Qwen2-VL-2B multimodal language model for text-to-CAD and image-to-CAD generation.

---

## Data Description

### `dataset/` — Processed Data

| File | Description |
|------|-------------|
| `featurescript_fp.zip` | Full-precision processed FeatureScript programs. |
| `featurescript_rp.zip` | Used in training and evaluation. Reduced-precision variant with floating-point values rounded to 2 decimal places. Produces a more compact token representation suitable for language model training. Note that rounding may break compilability for some models, while others are only compilable in reduced precision. |
| `text_annotations.zip` | Natural language annotations describing the geometry, topology, and design intent of each model, obtained with GPT-OSS-120b. |
| `step.zip` | STEP files with B-rep geometry. Rendered from `featurescript_fp.zip` where possible, or `featurescript_rp.zip` otherwise. |

### `raw/` — Raw Source Data

Source data used to create the processed FeatureScript representation.

| File | Description |
|------|-------------|
| `featurescript_raw.zip` | Unprocessed FeatureScript programs. |
| `sketch_raw.zip` | Sketch metadata extracted from FeatureScript feature trees. |
| `step_abc.zip` | STEP files extracted from the ABC dataset, used in both training and evaluation. |
| `multiview_images_abc.zip` | Multi-view images rendered from `step_abc.zip`, used in both training and evaluation. |

### `test_data/` — Evaluation Benchmarks

Minimal data required to perform evaluation, without the need to download the full dataset. A `.json` metadata file and a `.zip` archive are provided for each benchmark:

| Benchmark | Contents |
|-----------|----------|
| **CADFS** | jsonl, fs, annotations, images, step |
| **DeepCAD** | jsonl, fs, annotations, images, step |
| **CADParser** | jsonl, images, step |

### `train_data/` — Training Splits

Training data is split into two stages following the CADFS two-stage fine-tuning strategy, and two input modalities each. We filtered out duplicates and kept only samples whose input and output fit within a context size of 8192 tokens.

| File | Stage | Modality | Description |
|------|-------|----------|-------------|
| `stage1_txt_train.jsonl` | Stage 1 | Text | Pre-training on text-to-FeatureScript generation |
| `stage1_img_train.jsonl` | Stage 1 | Image | Pre-training on image-to-FeatureScript generation |
| `stage2_txt_train.jsonl` | Stage 2 | Text | Fine-tuning with high-quality curated text pairs |
| `stage2_img_train.jsonl` | Stage 2 | Image | Fine-tuning with high-quality curated image-program pairs |

For training and evaluation we use `.jsonl` data format. Each `.jsonl` line follows the format:

**Image input:**
```json
{
  "messages": [
    {
      "role": "system",
      "content": "You are CAD code generation model."
    },
    {
      "role": "user",
      "content": "<image>Generate a CAD model using FeatureScript framework..."
    },
    {
      "role": "assistant",
      "content": "FeatureScript 1511;\n..."
    }
  ],
  "images": ["path/to/0085/00858269.png"],
  "cad_file_id": "00858269"
}
```

**Text input:**
```json
{
  "messages": [
    {
      "role": "system",
      "content": "You are CAD code generation model."
    },
    {
      "role": "user",
      "content": "Step 1 - Sketch\nCreate a new sketch on the default top plane..."
    },
    {
      "role": "assistant",
      "content": "FeatureScript 1511;\n..."
    }
  ],
  "cad_file_id": "00858269"
}
```

---

## Usage

For usage examples, inference code, and FeatureScript processing pipeline, see the [CADFS GitHub repository](https://github.com/VladPyatov/CADFS).

## License

This dataset is released under **[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)**.  
It may be used for any purpose, including commercial, with attribution.

---

## Citation

If you use this dataset in your research, please cite:

```bibtex
@inproceedings{pyatov2026cadfs,
    title      = {{{CADFS}}: A Big {{CAD}} Program Dataset and Framework for Computer-Aided Design with Large Language Models},
    shorttitle = {{{CADFS}}},
    booktitle  = {2026 {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
    author     = {Vladislav Pyatov and Gleb Bobrovskikh and Saveliy Galochkin and Nikita Boldyrev and Oleg Voynov and Alexander Filippov and Gonzalo Ferrer and Peter Wonka and Evgeny Burnaev},
    year       = 2026,
    month      = jun,
    langid     = {english}
}
```