README.md

metadata

title: DeepCAD Generator
emoji: 🔧
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.47.2
app_file: app.py
pinned: false
license: mit
python_version: 3.9

🔧 DeepCAD: Deep Generative Network for CAD Models

Generate Computer-Aided Design (CAD) models using deep learning! This Space implements the DeepCAD model from the ICCV 2021 paper.

📖 About

DeepCAD is a deep generative network that can automatically generate novel, high-quality CAD models. This Space provides an interactive demo where you can:

• 🎲 Generate random CAD models
• 🎯 Control generation with seed values for reproducibility
• 🌡️ Adjust temperature to control creativity
• 📊 View 2D visualizations of generated models
• 💾 Download H5 files for further processing

🚀 How to Use

1. Set Random Seed (0-10000): Controls which random model is generated
2. Adjust Temperature (0.1-2.0):
   • Lower values (0.5-0.8) = more conservative, structured designs
   • Higher values (1.2-2.0) = more creative, varied designs
3. Click "Generate CAD Model" to create a new design
4. Download the H5 file to use with full DeepCAD tools

📊 What You'll See

The visualization shows:

• 2D Projection: Simplified view of CAD operations
• Command Distribution: Histogram of operation types
• Parameter Statistics: Key metrics about the generated model

Note: This is a 2D simplified visualization. For full 3D STEP file export, download the H5 file and use the original DeepCAD repository tools.

🔬 Technical Details

Model Architecture

• Latent Dimension: 256
• Commands: 60 CAD operations per sequence
• Arguments: 256 parameters per command
• Device: Automatic CPU/GPU detection

Generation Process

1. Sample a random latent vector (controlled by seed)
2. Scale by temperature for variation
3. Decode through neural network
4. Output vectorized CAD sequence

💾 Using the Generated H5 Files

The H5 files are compatible with the original DeepCAD tools: ```bash