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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}
}
``` |