ID-Head checkpoints
ID-Head decides, for every ray of a query view, whether the depth that a frozen VGGT-1B reconstruction gives on that ray should be answered or withheld. It is a U-Net with three downsamplings (0.35M parameters) that maps the 18×90×160 feature map of a query view to one logit per ray, and it never changes the depth itself. Code and documentation are in the IDSet-3D GitHub repository; the model is defined in code/idhead/c76/c76_train.py (ThreeDownUNet) and evaluated by code/pool/c83_method.py.
Files
| File | Content |
|---|---|
pool/training/c76_v1b_seed2026102{5,6,7,8,9}.pt |
ID-Head, one checkpoint per training seed (the five registered checkpoints) |
pool/training/c79b_rebuild/no_evidence_seed2026102{5,6,7}.pt |
the same model trained without the predicted-evidence features (registered comparison) |
pool/training/gbdt_v0.joblib |
gradient-boosted-tree control on the same features (scikit-learn HistGradientBoostingClassifier) |
pool/training/c83_validation_thresholds.json |
answer thresholds fixed on the validation windows |
pool/training/**/*.json |
training logs of the files above |
pool/training/c79b_rebuild/{evidence_only,full_n25,full_n50}_seed2026102{5,6,7}.pt |
validation ablations: evidence features only, and 25 or 50 training windows; c79b_rebuild/suite.json and c80_unified_evaluation.json hold their validation results |
Download and load
The scripts read the checkpoints from pool/training/ under the repository root, so download into a clone of the GitHub repository:
pip install -U huggingface_hub
python - <<'EOF'
from huggingface_hub import snapshot_download
skip = ["README.md", ".gitattributes"] # keep the GitHub repository's README.md and .gitattributes
snapshot_download("wolfkingttf/IDSet3D-IDHead", local_dir=".", ignore_patterns=skip)
EOF
sha256sum -c checksums/IDSet3D-IDHead.sha256
Skipping README.md and .gitattributes keeps the files of the GitHub repository from being replaced.
import torch
ck = torch.load("pool/training/c76_v1b_seed20261025.pt", map_location="cpu", weights_only=False)
state_dict = ck["model"] # weights of ThreeDownUNet
The checkpoints are pickled Python dictionaries, and loading a pickle can run code. Compare the SHA256 with checksums/IDSet3D-IDHead.sha256, and for the registered files with prereg/IDSET_PREREGISTRATION_v1.2.json in the GitHub repository, before loading them. The files were written with PyTorch 2.7.1; the GBDT control with scikit-learn 1.7.2 and joblib 1.6.0, and other scikit-learn versions may fail to load it.
Inputs
The 18 features of code/pool/features.py, computed from the predictions of VGGT-1B (facebook/VGGT-1B, revision 860abec7937da0a4c03c41d3c269c366e82abdf9) on the eight input views of a window. The answer on a ray is the VGGT-1B readout depth scaled by 1.0096. Features and labels of the benchmark are in the dataset repository wolfkingttf/IDSet3D.
License
CC BY-NC 4.0. The checkpoints were trained on labels derived from MatrixCity (CC BY-NC 4.0) and on features of VGGT-1B predictions (CC BY-NC 4.0).