Asset Management
Robot models, datasets, checkpoints, and demo media are not tracked in Git. They are distributed via ModelScope and HuggingFace.
What's Not in Git
teleopit/retargeting/gmr/assets/- Robot meshes, URDF/MJCFdata/, checkpoints, caches- Demo media (
assets/demo.gif,assets/demo.mp4)
Repositories
ModelScope (default download source)
| Repository | Type | Contents |
|---|---|---|
BingqianWu/Teleopit-models | model | Checkpoints, GMR retargeting assets, sample BVH |
BingqianWu/Teleopit-datasets | dataset | Training/validation datasets |
HuggingFace (alternative)
| Repository | Type | Contents |
|---|---|---|
12e21/Teleopit-models | model | Checkpoints, GMR retargeting assets, sample BVH |
12e21/Teleopit-datasets | dataset | Training/validation datasets |
Asset Group Mapping
| Group | Repository | Remote Path |
|---|---|---|
ckpt | Teleopit-models | checkpoints/track.onnx, checkpoints/track.pt |
gmr | Teleopit-models | archives/gmr_assets.tar.gz |
bvh | Teleopit-models | archives/sample_bvh.tar.gz |
data | Teleopit-datasets | data/train/, data/val/ |
Download
Use the project download script (defaults to ModelScope):
# Download everything
python scripts/setup/download_assets.py
# Only inference essentials
python scripts/setup/download_assets.py --only gmr ckpt bvh
# Only training data
python scripts/setup/download_assets.py --only data
# Download from HuggingFace instead
python scripts/setup/download_assets.py --source huggingface
Local paths after download:
| Remote | Local |
|---|---|
checkpoints/track.onnx | track.onnx |
checkpoints/track.pt | track.pt |
archives/gmr_assets.tar.gz | teleopit/retargeting/gmr/assets/ (extracted) |
archives/sample_bvh.tar.gz | data/sample_bvh/ (extracted) |
data/train/ | data/datasets/seed/train/ |
data/val/ | data/datasets/seed/val/ |
Upload to ModelScope
Step 1: Prepare Upload Directory
python scripts/setup/prepare_modelscope_assets.py --only ckpt gmr bvh --clean
python scripts/setup/prepare_modelscope_assets.py --only data
Output goes to data/modelscope_upload/.
Step 2: Upload
# Model repo
modelscope upload --repo-type model BingqianWu/Teleopit-models \
data/modelscope_upload/checkpoints checkpoints
modelscope upload --repo-type model BingqianWu/Teleopit-models \
data/modelscope_upload/archives archives
# Dataset repo
modelscope upload --repo-type dataset BingqianWu/Teleopit-datasets \
data/modelscope_upload/data data
Step 3: Tag Version
Only the model repo supports tags (dataset repo does not).
python - <<'EOF'
from modelscope.hub.api import HubApi
api = HubApi()
url = api.create_model_tag("BingqianWu/Teleopit-models", "v0.2.0")
print(url)
EOF
Tags should match Git tags for traceability.
Upload to HuggingFace
Step 1: Prepare and Upload
# Prepare and upload model assets (--clean ensures no leftover files)
python scripts/setup/upload_hf_assets.py --only ckpt gmr bvh --clean
# Prepare and upload dataset
python scripts/setup/upload_hf_assets.py --only data --clean
Use --dry-run to stage files locally without uploading.
warning
Always use --clean when running --only, otherwise the staging directory may carry leftover files from a previous run, causing unintended uploads.
Step 2: Tag Version
python - <<'EOF'
from huggingface_hub import HfApi
api = HfApi()
api.create_tag("12e21/Teleopit-models", tag="v0.2.0", repo_type="model")
EOF
Pre-Push Check
python scripts/dev/check_large_tracked_files.py
This blocks large binary files and checks tracked file size limits.