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DEF-tuni
ilessio-aiflowlab/DEF-tuni
DEF-tuni is a machine learning model from ilessio-aiflowlab. 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 apache-2.0.
Paper: TUNI: Real-time RGB-T Semantic Segmentation with Unified Multi-Modal Feature Extraction and Cross-Modal Feature Fusion Venue: ICRA 2026 Authors: Xiaodong Guo et al.
Access
Public
Updated Apr 5, 2026
Repo size
470 MB
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1
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Repo makeup
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.pth128 MB · 33%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Apr 4, 2026
- Updated
- Apr 5, 2026
- SHA
- 7143de18
Model card
From the Hugging Face model README
TUNI: Real-Time RGB-T Semantic Segmentation
Paper: TUNI: Real-time RGB-T Semantic Segmentation with Unified Multi-Modal Feature Extraction and Cross-Modal Feature Fusion Venue: ICRA 2026 Authors: Xiaodong Guo et al.
Model
- Architecture: TUNI (Conv + Attention hybrid) with unified RGB-T encoder
- Variant: 384_2242 (dims=[48,96,192,384], depths=[2,2,4,2])
- Parameters: 10.6M
- Input: RGB (3×480×640) + Thermal (3×480×640)
- Output: Segmentation mask (480×640)
Performance
| Dataset | Classes | mIoU | Pixel Acc |
|---|---|---|---|
| FMB | 15 | 62.4% | 91.4% |
| PST900 | 5 | TBD | TBD |
| CART | 12 | TBD | TBD |
Available Formats
tuni_fmb.pth— PyTorch state dicttuni_fmb.safetensors— Safetensors formattuni_fmb.onnx+tuni_fmb.onnx.data— ONNX (opset 18)tuni_fmb_fp16.trt— TensorRT FP16 enginetuni_fmb_fp32.trt— TensorRT FP32 engine
Usage
from def_tuni.model import TUNIModel
model = TUNIModel(variant="384_2242", n_classes=15)
model.load_checkpoint("tuni_fmb.pth")
model.eval().cuda()
# Inference
pred = model(rgb_tensor, thermal_tensor)
segmentation = pred.argmax(dim=1)
ANIMA Module
Part of the ANIMA Defense Module ecosystem. Built with ANIMA by Robot Flow Labs.
- License
- apache-2.0
- Created
- Apr 4, 2026
- Updated
- Apr 5, 2026