Datasets:

yinbq
/

SMM-Dataset

title: SMM Dataset
license: mit
tags:
  - dataset
  - computer-vision
  - 3d
  - blocks
  - spatial-reasoning
  - minecraft
size_categories:
  - 10G<n<100G

SMM Dataset

This dataset contains 3D block manipulation and spatial reasoning data for training AI models on spatial understanding and block construction tasks.

📦 Download

The dataset is provided as a compressed archive:

File: semantic_blocks_part2.tar.gz
Size: 0.81 GB (compressed)
Format: GZ compressed tar archive

Extract the dataset:

# For .tar.gz files
tar -xzf semantic_blocks_part2.tar.gz

# For .tar.xz files
tar -xJf semantic_blocks_part2.tar.gz

# For .tar.bz2 files
tar -xjf semantic_blocks_part2.tar.gz

📊 Dataset Structure

After extraction, you will find:

SMM/
├── SMM_data/              # Main dataset files
├── random_pipeline/       # Random block generation pipeline
├── semantic_blocks_part1/ # First part of semantic block data
├── semantic_blocks_part2/ # Second part of semantic block data
└── sementic_blocks.tar.gz # Additional compressed semantic blocks

🎯 Usage

This dataset can be used for:

Training models on spatial reasoning
3D object manipulation and understanding
Block construction and planning tasks
Multi-view 3D reconstruction
Sequential decision making in 3D environments

📝 Data Format

The dataset contains:

Images: RGB images of block structures from multiple viewpoints
Annotations: Spatial relationships and block configurations
Metadata: Task descriptions and ground truth labels

🔗 Related Resources

Paper: [Link to paper if available]
Code: [Link to training/inference code]
Model: [Link to trained models]

📄 License

This dataset is released under the MIT License.

🙏 Citation

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

@dataset{smm_dataset,
  title={SMM Dataset: Spatial Reasoning with Minecraft Blocks},
  author={Your Name},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/yinbq/SMM-Dataset}
}

📧 Contact

For questions or issues, please open an issue on the dataset repository.

Note: This is a large dataset. Make sure you have sufficient disk space (~40GB) before extracting.