YAML Metadata Warning: The task_categories "computer-vision" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

3D Optical Flow DROID Dataset

Processed DROID robotics dataset with optical flow and scene flow annotations.

Dataset Structure

Organized by lab, each trajectory in separate tar.gz archive:

IPRL/IPRL+2023-06-19+Mon_Jun_19_23:27:48_2023.tar.gz
CLVR/CLVR+2023-...tar.gz
... (15 labs, ~33K trajectories)

Each trajectory contains:

metadata.json - Trajectory metadata
trajectory.h5 - Robot state and actions
camera_left/, camera_right/ - Camera data
- rgb/ - RGB images
- depth/ - Depth maps
- optical_flow_with_mask/ - 2D optical flow
- scene_flow/ - 3D scene flow

Usage

from huggingface_hub import hf_hub_download
import tarfile

# Download specific trajectory
tar_path = hf_hub_download(
    repo_id="Salesforce/3d_optical_flow_droid",
    filename="IPRL/IPRL+2023-06-19+Mon_Jun_19_23:27:48_2023.tar.gz",
    repo_type="dataset"
)

# Extract
with tarfile.open(tar_path, "r:gz") as tar:
    tar.extractall("./data")

Stats

Trajectories: ~33,108
Size: ~26 TB (compressed)
Labs: 15 robotics labs
Frames: ~600-700 per trajectory

Citation

@article{droid2024,
  title={DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset},
  year={2024}
}

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