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DM nuScenes — release 1007

Driving data from the DM test vehicle (an Ouster LiDAR, 14 RGB cameras and 2 thermal cameras, GNSS/INS) in the nuScenes v1.0-trainval format: load it with the nuscenes-devkit as is.

637 scenes of 20 s (3.54 h) from 105 recordings · 25,480 key-frame samples · 6,081,808 sensor frames · 1557 GB to download in 245 sensor tars + 1 table tar (1658 GB unpacked). No 3D box annotations (sample_annotation is empty).

This release holds the recordings delivered so far (amsa, chuncheon, gangbyeonbungno, gangnam, gapyeong, geondae, godeok, gosokdoro, namyangju, seongsu, wangsimni, yanggu); the tables list exactly the scenes whose files are in it. More recordings are added by later releases.

Download and use

  1. Log in if the dataset is gated or private: hf auth login.
  2. Download this release and put it together (assemble.py checks every tar's sha256 and extracts it):
pip install -U huggingface_hub
hf download YewonSong/DataMachine --repo-type dataset --revision 1007 --local-dir dm_tars
python dm_tars/assemble.py --tars dm_tars --out dm_nuscenes
from nuscenes.nuscenes import NuScenes
from nuscenes.can_bus.can_bus_api import NuScenesCanBus
nusc = NuScenes(version="v1.0-trainval", dataroot="dm_nuscenes")
can = NuScenesCanBus(dataroot="dm_nuscenes")      # pose, ms_imu, meta per scene

Some recordings only: download the tables and the recordings you want, and let assemble.py skip the rest (the tables still list every scene of the release; the files of the others are just absent):

hf download YewonSong/DataMachine --repo-type dataset --revision 1007 --local-dir dm_tars \
    --include "manifest.json" --include "assemble.py" --include "meta/*" --include "sensors/rec_20260929_162450/*"
python dm_tars/assemble.py --tars dm_tars --out dm_nuscenes --allow-missing

Updating: Coming from 1003: run the two commands above in the same directories — only the tars new in 1007 are downloaded and extracted. Nothing already extracted is changed; the tables are replaced. Add --delete-tars to assemble.py to remove each tar after extracting it (then hf download fetches it again next time).

Releases and versioning

Each release is a branch of this repository (--revision <name>), a complete dataset on its own; a release is never changed after it is published. Later releases only add data:

  • sensors/<recording>/<recording>.partNN.tar.gz — the sensor files (samples/, sweeps/) of a group of whole scenes of one recording, at most 10 GB unpacked (gzip: the LiDAR files shrink by about a third, the images not). Once published, a tar never changes and is carried into every later release (stored once).
  • meta/DM_meta.tar.gz — the tables (v1.0-trainval/) of the scenes released so far, can_bus/, maps/ and the per-recording *.import.json (how each recording was converted; the driver's and passenger's names are removed). The only file that changes between releases.
  • manifest.json — every tar with its recording, scene tokens, files, bytes and sha256, and the release history.
  • Scene tokens never change. Scene names (scene-0001, …) are the nuScenes train split names in the order the recordings were converted, so they are not contiguous inside one release; the names of released scenes do not change. All scenes are in the train split.
release date locations added recordings added scenes added scenes in total parser commit
1002 2026-10-03 amsa 4 80 80 56ad0fe (+local changes)
1003 2026-10-06 gangbyeonbungno, godeok 13 121 201 56ad0fe (+local changes)
1007 2026-10-08 chuncheon, gangbyeonbungno, gangnam, gapyeong, geondae, gosokdoro, namyangju, seongsu, wangsimni, yanggu 88 436 637 56ad0fe (+local changes)

Contents

location dates recordings scenes time GB
amsa 2026-09-29 4 80 27 min 202
chuncheon 2026-10-01 4 10 3 min 14
gangbyeonbungno 2026-09-29, 2026-10-03 7 7 2 min 13
gangnam 2026-10-02 9 154 51 min 413
gapyeong 2026-10-01 2 3 1 min 6
geondae 2026-10-01 15 80 27 min 208
godeok 2026-09-29 7 115 38 min 300
gosokdoro 2026-10-03 3 4 1 min 4
namyangju 2026-10-03 34 64 21 min 143
seongsu 2026-10-01 9 58 19 min 143
wangsimni 2026-10-01 4 26 9 min 69
yanggu 2026-10-03 7 36 12 min 42
channel sensor data rate frames
CAM_FRONT_1 camera 1920×1200 JPG 29 Hz 373,280
CAM_FRONT_2 camera 1920×1200 JPG 29 Hz 373,278
CAM_FRONT_3 camera 1920×1200 JPG 29 Hz 373,280
CAM_FRONT_4 camera 1920×1200 JPG 29 Hz 373,280
CAM_FRONT_5 camera 1920×1200 JPG 29 Hz 373,280
CAM_FRONT_6 camera 1920×1200 JPG 29 Hz 373,280
CAM_FRONT_7 camera 1920×1200 JPG 29 Hz 373,280
CAM_FRONT_8 camera 1920×1200 JPG 29 Hz 373,280
CAM_FRONT_9 camera 1920×1200 JPG 29 Hz 373,280
CAM_REAR_LEFT camera 1920×1200 JPG 29 Hz 373,280
CAM_REAR_RIGHT camera 1920×1200 JPG 29 Hz 373,280
CAM_SIDE_LEFT camera 1920×1200 JPG 29 Hz 373,280
CAM_SIDE_RIGHT camera 1920×1200 JPG 29 Hz 373,280
CAM_THERMAL_LEFT thermal camera 640×480 PNG 29 Hz 365,341
CAM_THERMAL_RIGHT thermal camera 640×480 PNG 29 Hz 365,697
CAM_TOP camera 1920×1200 JPG 29 Hz 373,280
LIDAR_TOP lidar point cloud (.pcd.bin) 10 Hz 124,852

Key frames (samples/) are 2 Hz, synchronised across the sensors as in nuScenes; every other frame is a sweep (sweeps/). LiDAR files hold five float32 per point (x, y, z, intensity, ring), as in nuScenes. The camera images are stored as recorded (not rectified): calibrated_sensor holds camera_intrinsic and, for the cameras, distortion_model and camera_distortion (lens model and coefficients), plus the extrinsics, one set per recording.

Coordinate frames and the quality of the pose

ego_pose is in the global frame of the recording's location, as in nuScenes: metres, x east, y north, z up, on the tangent plane at that location's origin (height 0 on the WGS84 ellipsoid). log.json names it in the extra key global_frame (enu@37.550000,127.140000,0.000) and location (korea-amsa, korea-godeok, ...). Each location has its own origin: positions of different locations are not in one plane.

The pose is poor. It comes from the vehicle's GNSS/INS (/gps/fix, /imu/data, /gps/vel; 100 Hz), and the receiver often has no GNSS solution (the pose is then the INS coasting), so absolute positions can drift or jump and the speed can be off. Per-scene figures are in <recording>.import.json (pos_type, gnss_solution_by_scene, position_sigma_m_median). Use the poses for what they are good for (ego orientation, short-term motion), not as ground truth; the IMU (can_bus/<scene>_ms_imu.json) and the LiDAR are more reliable than the position.

Recordings

recording location date scenes tars GB first release
rec_20260929_144612 godeok 2026-09-29 23 7 54.9 1003
rec_20260929_150138 godeok 2026-09-29 14 5 36.0 1003
rec_20260929_151129 godeok 2026-09-29 21 7 54.7 1003
rec_20260929_152855-카메라2죽음 godeok 2026-09-29 7 3 19.1 1003
rec_20260929_154234 godeok 2026-09-29 25 8 68.4 1003
rec_20260929_155714 godeok 2026-09-29 14 5 38.3 1003
rec_20260929_160819 godeok 2026-09-29 11 4 28.7 1003
rec_20260929_162450 amsa 2026-09-29 25 9 64.8 1002
rec_20260929_163610 amsa 2026-09-29 19 6 45.4 1002
rec_20260929_164535 amsa 2026-09-29 16 6 40.4 1002
rec_20260929_165424 amsa 2026-09-29 20 7 51.9 1002
clip_20260929_171328 gangbyeonbungno 2026-09-29 1 1 1.7 1003
clip_20260929_171941 gangbyeonbungno 2026-09-29 1 1 2.1 1003
clip_20260929_172040 gangbyeonbungno 2026-09-29 1 1 2.0 1003
clip_20260929_172123 gangbyeonbungno 2026-09-29 1 1 1.9 1003
clip_20260929_172245 gangbyeonbungno 2026-09-29 1 1 1.9 1003
clip_20260929_172502 gangbyeonbungno 2026-09-29 1 1 2.0 1003
rec_20261001_193910 seongsu 2026-10-01 10 4 27.2 1007
rec_20261001_194603 seongsu 2026-10-01 14 5 37.0 1007
rec_20261001_195123 seongsu 2026-10-01 3 1 7.0 1007
rec_20261001_195257 seongsu 2026-10-01 2 1 4.3 1007
rec_20261001_195915 geondae 2026-10-01 2 1 5.1 1007
rec_20261001_200100 geondae 2026-10-01 12 4 34.4 1007
rec_20261001_200556 geondae 2026-10-01 12 4 34.5 1007
rec_20261001_201052 geondae 2026-10-01 2 1 4.8 1007
rec_20261001_201333 geondae 2026-10-01 10 3 21.9 1007
rec_20261001_202047 geondae 2026-10-01 1 1 2.4 1007
rec_20261001_202153 geondae 2026-10-01 7 3 18.7 1007
rec_20261001_202525 geondae 2026-10-01 1 1 2.1 1007
rec_20261001_202626 geondae 2026-10-01 4 2 10.8 1007
rec_20261001_202817 geondae 2026-10-01 4 1 9.2 1007
rec_20261001_203119 geondae 2026-10-01 1 1 2.4 1007
rec_20261001_203252 geondae 2026-10-01 11 4 31.9 1007
rec_20261001_203908 geondae 2026-10-01 2 1 5.1 1007
rec_20261001_203959 geondae 2026-10-01 7 2 14.1 1007
rec_20261001_204300 geondae 2026-10-01 4 2 10.8 1007
rec_20261001_204850 seongsu 2026-10-01 3 1 6.3 1007
rec_20261001_205137 seongsu 2026-10-01 13 4 30.8 1007
rec_20261001_210116 seongsu 2026-10-01 5 2 11.6 1007
rec_20261001_210622 seongsu 2026-10-01 5 2 12.3 1007
rec_20261001_211024 seongsu 2026-10-01 3 1 6.7 1007
rec_20261001_211509 wangsimni 2026-10-01 2 1 4.1 1007
rec_20261001_211716 wangsimni 2026-10-01 3 1 8.6 1007
rec_20261001_211842 wangsimni 2026-10-01 13 5 34.1 1007
rec_20261001_212356 wangsimni 2026-10-01 8 3 21.7 1007
rec_20261002_063935 chuncheon 2026-10-01 3 1 3.8 1007
rec_20261002_065008 chuncheon 2026-10-01 2 1 2.7 1007
rec_20261002_070000 chuncheon 2026-10-01 1 1 1.4 1007
rec_20261002_070515 chuncheon 2026-10-01 4 1 5.9 1007
rec_20261002_075439 gapyeong 2026-10-01 1 1 1.8 1007
rec_20261002_080046 gapyeong 2026-10-01 2 1 4.2 1007
rec_20261002_122400 gangnam 2026-10-02 31 11 79.3 1007
rec_20261002_124459 gangnam 2026-10-02 7 3 18.1 1007
rec_20261002_130230 gangnam 2026-10-02 29 9 75.6 1007
rec_20261002_131637 gangnam 2026-10-02 7 3 20.4 1007
rec_20261002_132644 gangnam 2026-10-02 32 11 91.0 1007
rec_20261002_134111 gangnam 2026-10-02 10 4 28.6 1007
rec_20261002_134922 gangnam 2026-10-02 10 4 25.2 1007
rec_20261002_144625 gangnam 2026-10-02 3 1 7.5 1007
rec_20261002_144911 gangnam 2026-10-02 25 9 67.0 1007
clip_20261003_155736 namyangju 2026-10-03 1 1 2.3 1007
clip_20261003_155916 namyangju 2026-10-03 1 1 2.4 1007
rec_20261003_160515 namyangju 2026-10-03 3 1 6.6 1007
rec_20261003_161221 namyangju 2026-10-03 1 1 2.2 1007
rec_20261003_161922 namyangju 2026-10-03 1 1 2.2 1007
rec_20261003_162600 namyangju 2026-10-03 2 1 4.4 1007
clip_20261003_162808 namyangju 2026-10-03 1 1 2.3 1007
clip_20261003_163137 namyangju 2026-10-03 1 1 2.4 1007
rec_20261003_164207 namyangju 2026-10-03 4 1 9.0 1007
rec_20261003_164412 namyangju 2026-10-03 4 2 9.8 1007
rec_20261003_164612 namyangju 2026-10-03 1 1 2.4 1007
rec_20261003_164748 namyangju 2026-10-03 1 1 2.7 1007
rec_20261003_164852 namyangju 2026-10-03 1 1 2.5 1007
rec_20261003_165057 namyangju 2026-10-03 2 1 4.7 1007
rec_20261003_165205 namyangju 2026-10-03 1 1 2.4 1007
rec_20261003_165423 namyangju 2026-10-03 1 1 2.3 1007
rec_20261003_165623 namyangju 2026-10-03 2 1 4.9 1007
rec_20261003_165830 namyangju 2026-10-03 2 1 5.1 1007
rec_20261003_185214 namyangju 2026-10-03 1 1 2.4 1007
rec_20261003_185348 namyangju 2026-10-03 3 1 6.2 1007
rec_20261003_185713 namyangju 2026-10-03 4 1 7.3 1007
clip_20261003_190137 namyangju 2026-10-03 1 1 2.4 1007
rec_20261003_190352 namyangju 2026-10-03 1 1 2.7 1007
rec_20261003_190449 namyangju 2026-10-03 3 1 8.3 1007
rec_20261003_190606 namyangju 2026-10-03 1 1 2.5 1007
rec_20261003_190744 namyangju 2026-10-03 1 1 2.3 1007
rec_20261003_191016 namyangju 2026-10-03 9 3 19.1 1007
rec_20261003_191425 namyangju 2026-10-03 2 1 4.2 1007
rec_20261003_191521 namyangju 2026-10-03 1 1 2.4 1007
rec_20261003_191655 namyangju 2026-10-03 1 1 2.0 1007
rec_20261003_191738 namyangju 2026-10-03 2 1 3.6 1007
rec_20261003_191919 namyangju 2026-10-03 1 1 1.7 1007
rec_20261003_192014 namyangju 2026-10-03 2 1 3.5 1007
rec_20261003_192237 namyangju 2026-10-03 1 1 2.0 1007
clip_20261003_193542 gangbyeonbungno 2026-10-03 1 1 1.8 1007
rec_20261004_033655 gosokdoro 2026-10-03 1 1 1.2 1007
rec_20261004_035019 gosokdoro 2026-10-03 1 1 1.1 1007
rec_20261004_051712 gosokdoro 2026-10-03 2 1 2.1 1007
rec_20261004_053533 yanggu 2026-10-03 7 1 7.1 1007
rec_20261004_055315 yanggu 2026-10-03 12 2 14.2 1007
rec_20261004_060022 yanggu 2026-10-03 6 1 6.4 1007
rec_20261004_062938 yanggu 2026-10-03 2 1 2.8 1007
rec_20261004_064254 yanggu 2026-10-03 5 1 6.4 1007
rec_20261004_064745 yanggu 2026-10-03 3 1 3.8 1007
rec_20261004_065138 yanggu 2026-10-03 1 1 1.2 1007

Maintainers: making the next release

Releases are made with parser/scripts/hf_release.py of the DM parser (IRCV-DM-Ops; the commit of each release is in the table above):

# new recordings: parse them, curate them in the DM Parser app, press 최종 확정, then
python parser/scripts/hf_release.py --branch <new> --from 1007 --location <place> --token-file <hf token file>            # plan
python parser/scripts/hf_release.py --branch <new> --from 1007 --location <place> --token-file <hf token file> --upload   # build + upload

It refuses to upload unless the scenes it adds are locked and the dataset passes the checks (table references, devkit, CAN bus), and unless every tar already published still matches the dataset. Only the new recordings' tars are built and uploaded (one commit each, resumable); the tables, manifest, this card and assemble.py go last. First release of this series: 1002.

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