---
license: other
tags:
- Text
- Image
- 3D
- OpenSCAD
- CAD
- text-to-code
- 3d-modeling
- multimodal
size_categories:
- 10K<n<100K
task_categories:
- image-to-3d
- text-to-3d
language:
- en
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: Prompt
    dtype: string
  - name: Code
    dtype: string
  - name: Image
    dtype: image
  splits:
  - name: train
    num_bytes: 2347493427
    num_examples: 59999
  download_size: 2131668876
  dataset_size: 2347493427
---

![2](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/8rnEYcDMIlhRBpVorSlli.gif)

## **Text-to-Code-3D-Samples**

Text-to-Code-3D-Samples is a medium-scale multimodal 3D dataset containing **~59,999** paired entries of natural language design specifications, executable OpenSCAD (Open-source Solid 3D CAD Modeller) programmatic source scripts, and 3D visual render representations. **Converted and curated into Apache Parquet format from publicly accessible CAD model repositories**, open-source 3D asset libraries, and synthetic parametric definitions, this dataset is designed for training, supervised fine-tuning (SFT), and evaluating Code-LLMs and Vision-Language Models (VLMs) on parametric CAD synthesis (Text-to-CAD, Image-to-CAD, and CAD code generation).

> This is an experimental dataset only and may contain artifacts.

- **Total Samples:** ~59,999 rows
- **Format:** Apache Parquet (`Prompt`, `Code`, `Image`)
- **Modalities:** Text, Code, Image
- **Split:** Train

## Dataset Structure & Schema

### Feature Fields

| Field | Type | Description |
| :--- | :--- | :--- |
| `Prompt` | `string` | Technical description detailing dimensions, physical functional parts, and structural topology |
| `Code` | `string` | Executable programmatic OpenSCAD code script implementing constructive solid geometry (CSG) |
| `Image` | `Image` | Rendered 2D isometric/orthographic preview of the generated 3D solid model |

### Data Instance Example

```json
{
  "Prompt": "Design a parametric mounting bracket with four counter-sunk mounting holes along a rectangular base and a reinforced center rib.",
  "Code": "difference() { union() { cube([60, 40, 5], center=true); translate([0, 0, 15]) cube([10, 40, 25], center=true); } for(x=[-20, 20], y=[-12, 12]) translate([x, y, -3]) cylinder(h=8, r=2.5, $fn=30); }",
  "Image": "<PIL.PngImagePlugin.PngImageFile image mode="RGB" size="256x256">"
}
```

## How to Use

### Loading with `datasets`

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("prithivMLmods/Text-to-Code-3D-Samples", split="train")

# Access a single record
sample = dataset[0]
prompt = sample["Prompt"]
code = sample["Code"]
rendered_image = sample["Image"]

print("Prompt:\n", prompt)
print("\nOpenSCAD Code:\n", code)
```

### Instruction Fine-Tuning Format

```python
def format_cad_instruction(example):
    instruction = (
        "You are an expert mechanical engineer and CAD programmer. Write compilable "
        "OpenSCAD code that builds the 3D geometry specified below.\n\n"
        f"Specification: {example['Prompt']}"
    )
    
    return {
        "prompt": instruction,
        "completion": example["Code"]
    }

formatted_dataset = dataset.map(format_cad_instruction)
```

## Intended Uses 

* **Parametric CAD Generation:** Fine-tuning Code-LLMs (e.g., CodeLlama, Qwen-Coder, DeepSeek-Coder) to translate natural language engineering requests into compilable OpenSCAD scripts.
* **Multimodal Reverse-CAD:** Training Vision-Language Models to reverse-engineer constructive solid geometry (CSG) code directly from multi-view or isometric renders.
* **Rapid Prototyping & Additive Manufacturing:** Providing structured references for automating custom 3D printing components, brackets, enclosures, and adapters.