llama2thedog/BIM-JEPA-finetuned-ifcnetcore

BIM-JEPA (Finetuned on IFCNetCore)

Classification model finetuned from llama2thedog/BIM-JEPA-pretrained on the IFCNetCore dataset.

Performance (test set)

Metric	Value
Overall Accuracy	89.37%
Mean Class Accuracy (macro recall)	86.63%
Precision (macro)	89.38%
Recall (micro)	89.37%
F1 Score (macro)	87.69%

Paper

Self-supervised learning for BIM element classification using a joint embedding predictive architecture
Jack Wei Lun Shi, Wawan Solihin, Yufeng Weng, Yimin Zhao, Leong Hien Poh, Justin K.W. Yeoh
Automation in Construction

• GitHub repo (code + configs)
• Project page
• Pretrained base

Architecture

• Backbone: BIM-JEPA encoder (12-layer Transformer, 384 dim, 6 heads) — initialized from the pretrained checkpoint
• Head: MLP classifier, 256 hidden dim, mean+max pooling, 0.5 dropout
• Loss: Cross-entropy with label smoothing (0.1)
• Encoder schedule: Frozen for the first 175 epochs, then unfrozen and finetuned end-to-end

Training

Dataset	IFCNetCore
Input	4096 points per object
Augmentations	Scale, rotate (all axes), translate
Epochs	350
Batch size	32
Optimizer	AdamW (head lr=1e-3, encoder lr=1e-4, weight decay 0.05)
Schedule	Linear warmup (10 epochs) + cosine decay
Precision	bf16-mixed

Full training hyperparameters are in hparams.yaml.

Files

File	Size	Description
bim_jepa_finetuned_ifcnetcore.ckpt	256 MB	PyTorch Lightning checkpoint
hparams.yaml	2 KB	Training hyperparameters

Usage

Clone the GitHub repo first to get the model code, then:

from huggingface_hub import hf_hub_download
from bimjepa.models.classification import BimJepaClassification

ckpt_path = hf_hub_download(
    repo_id="llama2thedog/BIM-JEPA-finetuned-ifcnetcore",
    filename="bim_jepa_finetuned_ifcnetcore.ckpt",
)
model = BimJepaClassification.load_from_checkpoint(ckpt_path)
model.eval()

Citation

in progress

License

MIT