README.md

metadata

language:
  - en
task_categories:
  - image-text-to-text
tags:
  - robotics
  - spatial-reasoning
  - warehouse
  - depth-perception
  - multimodal
  - vision-language-model
dataset_info:
  features:
    - name: id_db
      dtype: string
    - name: id
      dtype: string
    - name: rgb_image
      dtype: string
    - name: depth_image
      dtype: string
    - name: rle
      list:
        - name: size
          list: int32
        - name: counts
          dtype: string
    - name: texts
      struct:
        - name: user
          dtype: string
        - name: assistant
          dtype: string
    - name: category
      dtype: string
    - name: normalized_answer
      dtype: string
    - name: dataset_name
      dtype: string
  splits:
    - name: train
      num_bytes: 2647453621
      num_examples: 499083
    - name: validation
      num_bytes: 13372284
      num_examples: 1942
  download_size: 1428260287
  dataset_size: 2660825905
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*

SmolRGPT Dataset: Efficient Spatial Reasoning for Warehouse Environments

This repository hosts the Spacial Warehouse Dataset, a key component for the research presented in:

• Paper: SmolRGPT: Efficient Spatial Reasoning for Warehouse Environments with 600M Parameters
• Code: https://github.com/abtraore/SmolRGPT

Abstract

Recent advances in vision-language models (VLMs) have enabled powerful multimodal reasoning, but state-of-the-art approaches typically rely on extremely large models with prohibitive computational and memory requirements. This makes their deployment challenging in resource-constrained environments such as warehouses, robotics, and industrial applications, where both efficiency and robust spatial understanding are critical. In this work, we present SmolRGPT, a compact vision-language architecture that explicitly incorporates region-level spatial reasoning by integrating both RGB and depth cues. SmolRGPT employs a three-stage curriculum that progressively align visual and language features, enables spatial relationship understanding, and adapts to task-specific datasets. We demonstrate that with only 600M parameters, SmolRGPT achieves competitive results on challenging warehouse spatial reasoning benchmarks, matching or exceeding the performance of much larger alternatives. These findings highlight the potential for efficient, deployable multimodal intelligence in real-world settings without sacrificing core spatial reasoning capabilities. The code of the experimentation will be available at: this https URL

Dataset Overview

The Spacial Warehouse Dataset is a crucial benchmark for challenging warehouse spatial reasoning tasks, integrating both RGB and depth cues for explicit region-level spatial understanding. It is specifically designed to support the training and evaluation of models like SmolRGPT, enabling efficient multimodal intelligence in resource-constrained environments such as warehouses and robotics.

Data Download and Preparation

To use this dataset, you first need to clone it from Hugging Face using Git LFS, and then download and untar the RGB and Depth images from the original repository as instructed in the SmolRGPT GitHub repository.

1. Clone the dataset:

   git lfs install # Make sure git-lfs is installed (https://git-lfs.com)
   git clone https://huggingface.co/datasets/Abdrah/warehouse-rgbd-smolRGPT
   

2. Download and untar RGB and Depth images:

   # You can also use `huggingface-cli download`
   git clone https://huggingface.co/datasets/nvidia/PhysicalAI-Spatial-Intelligence-Warehouse
   cd PhysicalAI-Spatial-Intelligence-Warehouse
   
   # Untar images for train/test subsets
   for dir in train test; do
       for subdir in images depths; do
           if [ -d "$dir/$subdir" ]; then
               echo "Processing $dir/$subdir"
               cd "$dir/$subdir"
               tar -xzf chunk_*.tar.gz
               # rm chunk_*.tar.gz
               cd ../..
           fi
       done
   done
   

Citation

@article{traore2025smolrgptefficientspatialreasoning,
      title={SmolRGPT: Efficient Spatial Reasoning for Warehouse Environments with 600M Parameters}, 
      author={Abdarahmane Traore and Éric Hervet and Andy Couturier},
      year={2025},
      eprint={2509.15490},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.15490}, 
}