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ReVQom
scdrand23/ReVQom
ReVQom is a machine learning model from scdrand23. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Pretrained checkpoints for ReVQom, a learned feature codec for multi-agent collaborative perception. ReVQom compresses BEV features via a 1x1 bottleneck and multi-stage residual vector quantization, transmitting only…
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Updated Aug 9, 2026
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At a glance
- License
- mit
- Access
- Public
- Created
- Aug 9, 2026
- Updated
- Aug 9, 2026
- SHA
- 6e0c7c43
Model card
From the Hugging Face model README
ReVQom: Residual Vector Quantization for Communication-Efficient Multi-Agent Perception (ICASSP 2026)
Pretrained checkpoints for ReVQom, a learned feature codec for multi-agent collaborative perception. ReVQom compresses BEV features via a 1x1 bottleneck and multi-stage residual vector quantization, transmitting only per-pixel code indices (6-30 bits per pixel, 273x-1365x compression vs raw features).
Checkpoints
| File | Config | Dataset | [email protected]/[email protected] |
|---|---|---|---|
revqom_s_k64_dairv2x/net_epoch30.pth | ReVQom-S: K=64, n_q=3, C_rr=16, EMA 0.8 (18 bpp, 455x) | DAIR-V2X | 0.751/0.646 |
The checkpoint directory includes the exact training config.yaml, and the evaluation matches the paper's experiment records. Additional checkpoints (ReVQom-M, K=256) will be added after re-evaluation.
Usage
pip install -U huggingface_hub
hf download scdrand23/ReVQom --local-dir checkpoints
python revqom/tools/inference.py --model_dir checkpoints/revqom_s_k64_dairv2x --fusion_method intermediate
See the GitHub repository for installation and dataset preparation.
Citation
@inproceedings{shenkut2026revqom,
title={Residual Vector Quantization for Communication-Efficient Multi-Agent Perception},
author={Shenkut, Dereje and Kumar, B.V.K. Vijaya},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year={2026}
}
- License
- mit
- Created
- Aug 9, 2026
- Updated
- Aug 9, 2026