LeWAM/lewam-drawer-3view

LeWAM Drawer Cleanup — three views

Simulation training data used by LeWAM.

• Episodes: 1,026
• Frames: 298,235
• Camera columns: pixels, pixels_r0eih, pixels_r1eih
• Action dimensions: 22

Files

File	Bytes	Purpose
drawer_fixed.h5	18,340,768,820	Scene camera, actions and episode metadata
drawer_3view.h5	42,575,765,750	Wrist-camera supplement
drawer_multiview.h5	2,548	Portable multiview entry point; requires both companion HDF5 files

Download the entire repository and keep the HDF5 files together. The multiview entry point uses relative HDF5 links to its companion files; it contains no cluster-specific paths. Open drawer_multiview.h5 for all views.

Download and read

pip install huggingface_hub h5py hdf5plugin

from pathlib import Path
from huggingface_hub import snapshot_download
import hdf5plugin  # registers the image compression filters
import h5py

root = Path(snapshot_download("LeWAM/lewam-drawer-3view", repo_type="dataset"))
with h5py.File(root / "drawer_multiview.h5", "r") as data:
    print(list(data.keys()))
    image = data["pixels"][0]
    action = data["action"][0]

Integrity and format

manifest.json records the byte size and SHA-256 of every HDF5 file. Camera frames are 224 × 224 RGB. ep_offset and ep_len describe episode boundaries. No optional preload cache is required.

The companion data files preserve the source training data bytes. Multiview entry points only combine columns; no trajectories or camera views are synthesized.