maomao0819
/

BEVANet

@@ -1,84 +1,85 @@
----
-license: mit
-library_name: BEVANet
-tags:
-- real-time-semantic-segmentation
-- real-time
-- semantic-segmentation
-- computer-vision
-- pytorch
-datasets:
-- Cityscapes
-metrics:
-- mIoU
-- FPS
----
-<div align="center">
-<h1> BEVANet: Bilateral Efficient Visual Attention Network for Real-Time Semantic Segmentation (ICIP 2025 spotlight) </h1>
-[![arXiv paper](https://img.shields.io/badge/arXiv%20paper-2508.07300-red.svg?logo=arXiv)](https://arxiv.org/abs/2508.07300)
-[![ICIP paper](https://img.shields.io/badge/IEEE%20paper-ICIP25%20spotlight-blue.svg?logo=IEEE)](https://ieeexplore.ieee.org/document/11084676)
-[![GitHub Code](https://img.shields.io/badge/Code-GitHub-black.svg?logo=github)](https://github.com/maomao0819/BEVANet)
-[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
-[Ping-Mao Huang](https://www.linkedin.com/in/maomao0819/), I-Tien Chao, Ping-Chia Huang, Jia-Wei Liao, Yung-Yu Chuang<br>
-National Taiwan University
-</div>
-## Model Description
-- **Task**: Real-Time Semantic Segmentation
-- **Dataset**: Cityscapes
-- **Model**: BEVANet
-BEVANet semantic segmentation model trained on Cityscapes dataset. Achieves 81.0% mIoU with 32.8 FPS on RTX3090.
-## Performance
-- **mIoU**: 81.0%
-- **FPS**: 32.8 (RTX3090)
-- **Parameters**: 58.6M
-## Usage
-```python
-from models.BEVANet import BEVANet_SEG
-# Load model from specific branch
-model = BEVANet_SEG.from_pretrained(
-    "maomao0819/BEVANet",
-    revision="main"
-)
-# For main branch (no revision needed)
-# model = BEVANet_SEG.from_pretrained("maomao0819/BEVANet")
-```
-## All Model Variants
-| Model | Branch | Dataset | Task | Performance | FPS | Parameters |
-|-------|--------|---------|------|-------------|-----|------------|
-| BEVANet-S | `imagenet-bevanet-s` | ImageNet | Classification | 71.1% Top-1 | 198.6 | 16.3M |
-| BEVANet | `imagenet-bevanet` | ImageNet | Classification | 76.3% Top-1 | 82.3 | 57.4M |
-| BEVANet-S | `cityscapes-bevanet-s` | Cityscapes | Segmentation | 78.2% mIoU | 70.0 | 15.2M |
-| BEVANet | `main` | Cityscapes | Segmentation | 81.0% mIoU | 32.8 | 58.6M |
-| BEVANet-S | `camvid-bevanet-s` | CamVid | Segmentation | 83.1% mIoU | 79.4 | 15.2M |
-| BEVANet | `ade20k-bevanet` | ADE20K | Segmentation | 39.8% mIoU | 73.3 | 58.9M |
-## Citation
-If you use this model, please cite:
-```bibtex
-@inproceedings{huang2025bevanet,
-  title={Bevanet: Bilateral Efficient Visual Attention Network for Real-Time Semantic Segmentation},
-  author={Huang, Ping-Mao and Chao, I-Tien and Huang, Ping-Chia and Liao, Jia-Wei and Chuang, Yung-Yu},
-  booktitle={2025 IEEE International Conference on Image Processing (ICIP)},
-  pages={2778--2783},
-  year={2025},
-  organization={IEEE}
-}
-```

+---
+license: mit
+library_name: BEVANet
+tags:
+- image-segmentation
+- real-time-semantic-segmentation
+- real-time
+- semantic-segmentation
+- computer-vision
+- pytorch
+datasets:
+- Cityscapes
+metrics:
+- mIoU
+- FPS
+---
+<div align="center">
+<h1> BEVANet: Bilateral Efficient Visual Attention Network for Real-Time Semantic Segmentation (ICIP 2025 spotlight) </h1>
+[![arXiv paper](https://img.shields.io/badge/arXiv%20paper-2508.07300-red.svg?logo=arXiv)](https://arxiv.org/abs/2508.07300)
+[![ICIP paper](https://img.shields.io/badge/IEEE%20paper-ICIP25%20spotlight-blue.svg?logo=IEEE)](https://ieeexplore.ieee.org/document/11084676)
+[![GitHub Code](https://img.shields.io/badge/Code-GitHub-black.svg?logo=github)](https://github.com/maomao0819/BEVANet)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+[Ping-Mao Huang](https://www.linkedin.com/in/maomao0819/), I-Tien Chao, Ping-Chia Huang, Jia-Wei Liao, Yung-Yu Chuang<br>
+National Taiwan University
+</div>
+## Model Description
+- **Task**: Real-Time Semantic Segmentation
+- **Dataset**: Cityscapes
+- **Model**: BEVANet
+BEVANet semantic segmentation model trained on Cityscapes dataset. Achieves 81.0% mIoU with 32.8 FPS on RTX3090.
+## Performance
+- **mIoU**: 81.0%
+- **FPS**: 32.8 (RTX3090)
+- **Parameters**: 58.6M
+## Usage
+```python
+from models.BEVANet import BEVANet_SEG
+# Load model from specific branch
+model = BEVANet_SEG.from_pretrained(
+    "maomao0819/BEVANet",
+    revision="main"
+)
+# For main branch (no revision needed)
+# model = BEVANet_SEG.from_pretrained("maomao0819/BEVANet")
+```
+## All Model Variants
+| Model | Branch | Dataset | Task | Performance | FPS | Parameters |
+|-------|--------|---------|------|-------------|-----|------------|
+| BEVANet-S | `imagenet-bevanet-s` | ImageNet | Classification | 71.1% Top-1 | 198.6 | 16.3M |
+| BEVANet | `imagenet-bevanet` | ImageNet | Classification | 76.3% Top-1 | 82.3 | 57.4M |
+| BEVANet-S | `cityscapes-bevanet-s` | Cityscapes | Segmentation | 78.2% mIoU | 70.0 | 15.2M |
+| BEVANet | `main` | Cityscapes | Segmentation | 81.0% mIoU | 32.8 | 58.6M |
+| BEVANet-S | `camvid-bevanet-s` | CamVid | Segmentation | 83.1% mIoU | 79.4 | 15.2M |
+| BEVANet | `ade20k-bevanet` | ADE20K | Segmentation | 39.8% mIoU | 73.3 | 58.9M |
+## Citation
+If you use this model, please cite:
+```bibtex
+@inproceedings{huang2025bevanet,
+  title={Bevanet: Bilateral Efficient Visual Attention Network for Real-Time Semantic Segmentation},
+  author={Huang, Ping-Mao and Chao, I-Tien and Huang, Ping-Chia and Liao, Jia-Wei and Chuang, Yung-Yu},
+  booktitle={2025 IEEE International Conference on Image Processing (ICIP)},
+  pages={2778--2783},
+  year={2025},
+  organization={IEEE}
+}
+```