Dvidia/dvidia-training-examples

DVIDIA Training: synthetic examples

Two authored datasets for reproducing the DVIDIA Training v0.1.0 CPU pipeline: moving-disc videos for visual prediction and native simulation recordings with numerical movement labels. These are software research fixtures. They contain no human footage, shoe demonstrations, physical robot recordings or calibrated camera perception.

Training artifacts and measurements · Demo · Source and installation

Contents

Dataset	Source	Scale	Supervision
visual/source/	Procedurally drawn moving-disc MP4 videos	10 recordings, 6 seconds each; 60 seconds total	Visual next-frame targets; no action labels
native/source/	Ten separately executed native MuJoCo placement recordings with schematic MP4 videos	10 recordings; 116.5 seconds total	Timestamped privileged simulator state and authored inverse-kinematics joint targets

Videos are H.264, 160 × 128 RGB schematics sampled at 10 Hz. The native videos depict a simulated six-joint arm and rigid box; they are not camera renderings. The native control labels were collected at 20 ms control timestamps when an authored target was requested. Source seeds are 8000 through 8009.

Each source directory contains skillspace.training.json, example-receipt.json and videos/. Native source also contains actions/, evidence/ and task.skill.json. Each native sidecar contains a timestamp, 12 context values, 6 bounded goal/orientation error values and 6 joint-delta targets per sample. Its feature contract is:

q6_object_minus_tcp3_target_minus_tcp3__capped_goal_delta3_upright_orientation_delta3_v1

The action labels come from simulator state and authored targets, not from recovering actions from pixels. Media, sample and evidence hashes record declared consistency; the alignment declaration is not an independent attestation. The prepared intake receipt retains actions_validation: "unvalidated_numeric_contract"; the later movement trainer checks the strict sidecar contract, hashes and declared alignment. Neither stage independently attests the underlying evidence.

archives/visual-skillspace.zip and archives/native-skillspace.zip preserve the original source exports. visual/prepared/ and native/prepared/ preserve the measured dataset.json, source_manifest.json and every referenced data/… video or action sidecar from the published evidence. These prepared datasets can be supplied directly to DVIDIA Training; their recording split and media hashes remain inspectable.

Recorded splits

Both datasets use seed 17 and connected groups formed from shared recording IDs, session IDs, shoe-pair IDs when present, and exact video hashes. In these examples, each recording has its own declared recording/session group. The protocol's target ratios are 0.70/0.15/0.15; the recorded allocation is 7/2/1 groups.

Split	Recording IDs in each dataset	Visual duration	Native movement samples
Training	example-01, 02, 04, 05, 07, 08, 09	42 seconds	4,029
Development	example-00, 03	12 seconds	1,154
Test	example-06	6 seconds	621

Abbreviated IDs in the table retain the example- prefix. Training fits weights, development selects ridge regularization, and test is scored after selection. The visual learner samples 12 frames per recording: 84/24/12 frames and 77/22/11 within-recording transitions across training/development/test.

The ten native recordings total 5,804 movement samples. Samples within a recording are correlated. Separate recording groups and supplier-declared complete flags do not establish independent task trials, novelty or a sufficient number of demonstrations. example-06 is also the exact native scene used for the separate one-scene qualification; that qualification adds no new held-out scene.

Download and reproduce

This repository is an archive and JSON/MP4 dataset, without a packaged Hugging Face datasets.load_dataset builder. The tabular Dataset Viewer is disabled because the files include nested simulator records, provenance and training contracts; they are intended for DVIDIA's validated intake. Download it with huggingface_hub, then use DVIDIA Training's source intake. The following requires Python 3.12+ and system FFmpeg, including ffprobe.

python -m pip install huggingface_hub
python -m pip install https://github.com/Dvidia-Inference/dvidia-training/releases/download/v0.1.0/dvidia_training-0.1.0-py3-none-any.whl

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="Dvidia/dvidia-training-examples",
    repo_type="dataset",
    local_dir="examples",
)

Train and verify the visual result, using a fresh output directory:

dvidia-train run examples/archives/visual-skillspace.zip --seed 17 --output runs/visual
dvidia-train inspect runs/visual

To reuse the published prepared split and referenced media, replace the source argument with examples/visual/prepared/dataset.json (or examples/native/prepared/dataset.json for the native branch).

For the native movement branch and candidate capsule, add the optional simulation engine and supply the explicit task source:

python -m pip install mujoco==3.15.0
dvidia-train run examples/archives/native-skillspace.zip --seed 17 --task-source examples/native/source/task.skill.json --output runs/native
dvidia-train inspect runs/native

Software installation and repository download require network access. Training on the downloaded files is offline by default. Fitting and artifact inspection do not perform closed-loop qualification. See the native pilot procedure for the separate collect, train, install and exact-scene test. Numerical reproducibility can depend on installed decoders and numerical libraries.

Provenance, license and intended use

DVIDIA contributors authored the source generator and the synthetic recordings. These files originate in the 2026-10-08 footage-pipeline release. The release ledger declares MIT code and footage licenses and records an initial export-integration failure before the completed runs. The repository's LICENSE preserves the MIT copyright and permission notice.

Use these examples to inspect video intake, provenance records, grouped splits, small CPU fitting and simulation-only movement supervision. Their simple imagery, narrow authored trajectories and privileged numerical labels do not represent human behavior or the variety of real robot tasks. No human-video action bridge, shoe-organizing skill, unseen-scene generalization or physical robot qualification has been demonstrated. Reported model metrics and the single native qualification are documented separately in the model card.