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| Component | Architecture | Params |
|---|---|---|
| Image branch | HRNet-W48 (pretrained on COCO-Stuff) | ~77M |
| Point branch | PointTransformerV2 | ~30M |
| Adaptive fusion | Attention-based | ~5M |
| Decoder | Mask2Former-style | ~20M |
| Total | ~130M |
dpss_weights.pth — Clean inference weights (state_dict only, ~500MB)best_full.pth — Full training checkpoint with optimizer state (for resume)upstream_code.tar.gz — Upstream DPSS code bundle (all model + data loader code)training_metrics.json — sPQ history from training1import torch
2from huggingface_hub import hf_hub_download
3
4# Download weights
5weights_path = hf_hub_download("mohansshf/dpss-archcad", "dpss_weights.pth")
6
7# Load (requires the upstream code — included in upstream_code.tar.gz)
8ckpt = torch.load(weights_path, map_location="cpu")
9model.load_state_dict(ckpt["state_dict"])1# Clone the training code
2git clone https://github.com/ArchiAI-LAB/ArchCAD _upstream
3# Or use the bundled code:
4wget https://huggingface.co/mohansshf/dpss-archcad/resolve/main/upstream_code.tar.gz
5tar xzf upstream_code.tar.gz -C upstream/
6
7# Install deps
8pip install torch torchvision numpy scipy Pillow munch pyyaml tensorboardX tensorboard tqdm huggingface_hub
9pip install 'git+https://github.com/facebookresearch/detectron2.git'
10
11# Train (downloads dataset automatically from jackluoluo/ArchCAD)
12python train_portable.py --batch_size 8 --epochs 1001@article{Luo2025ArchCAD,
2 title={ArchCAD-400K: An Open Large-Scale Architectural CAD Dataset and New Baseline for Panoptic Symbol Spotting},
3 author={Luo R, Liu Z, Cheng T, et al.},
4 journal={arXiv preprint arXiv:2503.22346},
5 year={2025}
6}