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venice-h1
OdaxAI/venice-h1
venice-h1 is a image segmentation model from OdaxAI. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
[](https://arxiv.org/abs/2606.22546) [](https://github.com/odaxai/Venice-H1) [](https://opensource.org/licenses/MIT)
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At a glance
- Task
- Image Segmentation
- Library
- pytorch
- License
- mit
- Access
- Public
- Created
- Jun 21, 2026
- Updated
- Jun 23, 2026
- SHA
- 63a17e79
Base models
Model card
From the Hugging Face model README
Venice-H1: Failure-Aware Query Re-Ranking for Referring Image Segmentation
Nicolò Savioli, Ph.D. — OdaxAI Research
[email protected] · odaxai.com
Architecture Overview
Venice-H1 pipeline. A frozen DeRIS backbone generates N=10 candidate masks. Multi-scale grid signatures encode spatial quality. The Failure Re-Ranker gates intervention: it only overrides Query-0 when confident the default choice is wrong.
The Failure-Case Bottleneck
 |  |
| 7–18% of samples generate 40–68% of total error | Failure cases form a "triangle of opportunity" |
Multi-Scale Grid Signatures
Compact 675-dim spatial descriptors pooled at 4×4, 8×8, 16×16 grids per candidate mask.
Multi-scale grid cells inspired by entorhinal cortex representations.
Model Description
Venice-H1 is a lightweight, backbone-decoupled re-ranking module for Referring Image Segmentation (RIS). It detects when the default query selection fails and selects a better alternative using:
- Multi-Scale Grid Signatures: 4×4, 8×8, 16×16 spatial pooling → 675-dim descriptors
- Failure Gate: binary classifier predicting whether Query 0 is suboptimal
- Gain Predictor: IoU-regression head estimating improvement per alternative query
- Backbone-decoupled: works on cached features, no retraining of DeRIS required
Architecture: 3-layer pre-norm Transformer encoder, 8 heads, hidden_dim=512
Parameters: 11,296,258 (11.3M) — matches paper exactly
Results
On failure cases (where Venice-H1 intervenes)
Positive Δ across all 8 evaluation splits.
| Metric | Value |
|---|---|
| Parameters | 11,296,258 |
| Δ_fail (mIoU on failures) | +1.824 |
| AUC (failure detection) | 0.778 |
| Δ_full (overall mIoU) | +0.039 |
| Q0 mIoU | 86.469 |
| Selected mIoU | 86.509 |
| Oracle mIoU | 89.691 |
| Harmful-switch rate | < 0.6% |
Failure Gate Analysis
 |  |
| ROC curves across splits. AUC 0.78–0.82 | Coverage-risk trade-off at different τ |
Qualitative Results
Re-ranking on RefCOCO val. Each row: input, ground truth, default query (red, fails), Venice-H1 corrected selection (blue). Venice-H1 recovers IoU > 84% in all cases.
Ablation Study
| Configuration | Δ_fail | Gate AUC |
|---|---|---|
| BASE only (no grid) | +1.01 | 0.812 |
| 4×4 only | +1.01 | 0.821 |
| 8×8 only | +0.87 | 0.790 |
| 16×16 only | +1.00 | 0.828 |
| BASE + all grids (ours) | +1.22 | 0.807 |
Medical Cross-Domain Transfer
Zero-shot transfer to MS-CXR (+1.16 mIoU) and M3D-RefSeg-2D (+0.51 mIoU) without fine-tuning.
External Dependencies
| Component | Model | Paper |
|---|---|---|
| Backbone | DeRIS-L | Dai et al. (2025) |
| Visual Encoder | Swin-Large | Liu et al. (2021) |
| Language Encoder | BEiT-3 | Wang et al. (2023) |
| Mask Generator | Mask2Former | Cheng et al. (2022) |
Venice-H1 does not include these weights. You need a running DeRIS-L instance to extract features.
Quick Start
import torch
from huggingface_hub import hf_hub_download
ckpt_path = hf_hub_download(repo_id="OdaxAI/venice-h1", filename="venice_h1_deris_l.pt")
ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False)
print("Config:", ckpt["config"])
print("Metrics:", ckpt["metrics"])
print("Parameters:", sum(v.numel() for v in ckpt["model"].values() if hasattr(v, "numel")))
Reproduce paper results (no dataset needed):
git clone https://github.com/odaxai/Venice-H1.git
cd Venice-H1 && pip install -r requirements.txt && pip install -e .
python reproduce_results.py --verify_only
── Architecture Verification ───────────────────
Parameters : 11,296,258 ✓ MATCH
── Paper Cross-Check (RefCOCO val) ─────────────
✓ delta_fail : 1.8244 (paper: 1.824)
✓ auc_fail : 0.7776 (paper: 0.778)
✓ delta_full : 0.0392 (paper: 0.039)
Files in Repository
| File | Description |
|---|---|
venice_h1_deris_l.pt | Trained checkpoint — 11.3M params, DeRIS-L backbone |
venice_h1_deris_l_metrics.json | Full evaluation metrics |
config.yaml | Training hyperparameters |
venice_h1/ | Python package (model code) |
train.py | Training script |
evaluate.py | Evaluation script |
reproduce_results.py | One-command paper reproduction |
scripts/extract_features.py | Feature extraction from DeRIS-L |
Training Details
| Parameter | Value |
|---|---|
| Optimizer | AdamW |
| Learning rate | 5e-4 |
| Weight decay | 1e-4 |
| Batch size | 512 |
| Epochs | 20 |
| Scheduler | Cosine + 3 epoch warmup |
| Loss: L_gate | Focal BCE (γ=2.0) |
| Loss: L_gain | Smooth-L1 (λ=5.0) |
| Mixed precision | FP16 |
| Seed | 42 |
Citation
@article{savioli2026veniceh1,
title = {Venice-H1: Failure-Aware Query Re-Ranking with Multi-Scale Grid Signatures
for Referring Image Segmentation},
author = {Savioli, Nicol\`{o}},
journal = {arXiv preprint arXiv:2606.22546},
year = {2026},
note = {OdaxAI Research},
}
License
MIT License. © 2026 OdaxAI Research. All research conducted by Nicolò Savioli, Ph.D.
- Task
- Image Segmentation
- Library
- pytorch
- License
- mit
- Languages
- en
- Created
- Jun 21, 2026
- Updated
- Jun 23, 2026