Add l-reside-its checkpoint

README.md

@@ -1,3 +1,124 @@
 ---
 license: mit
+tags:
+- pytorch_model_hub_mixin
+- model_hub_mixin
+- image-dehazing
+- spiking-neural-network
+- computer-vision
+pipeline_tag: image-to-image
 ---
+
+# DehazeSNN
+
+> U-Net-Like Spiking Neural Networks for Single Image Dehazing
+
+DehazeSNN integrates a U-Net-like encoder-decoder architecture with Spiking Neural Networks (SNNs), using an Orthogonal Leaky-Integrate-and-Fire Block (OLIFBlock) for efficient cross-channel communication. This yields competitive dehazing quality with fewer parameters and MACs compared to Transformer-based methods.
+
+## Available Checkpoints
+
+| Revision | Size | Dataset | Load Command |
+|----------|------|---------|--------------|
+| `main` | L | RESIDE-ITS | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN")` |
+| `l-reside-its` | L | RESIDE-ITS | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="l-reside-its")` |
+| `l-reside-ots` | L | RESIDE-OTS | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="l-reside-ots")` |
+| `l-reside-6k` | L | RESIDE-6k | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="l-reside-6k")` |
+| `l-rs-haze` | L | RS-Haze | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="l-rs-haze")` |
+| `m-reside-its` | M | RESIDE-ITS | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="m-reside-its")` |
+| `m-reside-6k` | M | RESIDE-6k | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="m-reside-6k")` |
+| `m-rs-haze` | M | RS-Haze | `DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="m-rs-haze")` |
+
+## Quick Start
+
+### Installation
+
+```bash
+# Clone the DehazeSNN repository (for model code + custom CUDA kernels)
+git clone https://github.com/FengShaner/DehazeSNN.git
+cd DehazeSNN
+
+# Create environment (requires CUDA 12.x)
+conda create -n DehazeSNN python=3.11 -y
+conda activate DehazeSNN
+
+# Install PyTorch with CUDA 12.1
+conda install pytorch=2.1.2 torchvision pytorch-cuda=12.1 -c pytorch -c nvidia -y
+
+# Install dependencies
+pip install huggingface_hub safetensors cupy-cuda12x timm
+```
+
+### Load and Run Inference
+
+```python
+import torch
+from PIL import Image
+import numpy as np
+
+# Import from the cloned repository
+from models.hub import DehazeSNNHub
+
+# Load model (default: DehazeSNN-L trained on RESIDE-ITS)
+model = DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN")
+model.cuda().eval()
+
+# Load a different variant
+# model = DehazeSNNHub.from_pretrained("FengShaner/DehazeSNN", revision="m-reside-6k")
+
+# Prepare input image (RGB, normalized to [-1, 1])
+img = Image.open("hazy_image.jpg").convert("RGB")
+img_np = np.array(img).astype(np.float32) / 255.0
+img_tensor = torch.from_numpy(img_np).permute(2, 0, 1).unsqueeze(0)  # [1, 3, H, W]
+img_tensor = (img_tensor - 0.5) / 0.5  # normalize to [-1, 1]
+img_tensor = img_tensor.cuda()
+
+# Inference
+with torch.no_grad():
+    output = model(img_tensor).clamp(-1, 1)
+
+# Convert output back to image
+output = (output * 0.5 + 0.5).squeeze(0).permute(1, 2, 0).cpu().numpy()
+output = (output * 255).astype(np.uint8)
+Image.fromarray(output).save("dehazed_image.jpg")
+```
+
+## Requirements
+
+- **CUDA GPU required**: The custom LIF CUDA kernels require an NVIDIA GPU with CUDA support
+- **CuPy**: `cupy-cuda12x` (for CUDA 12.x) - CPU-only inference is **not supported**
+- PyTorch >= 2.1 with CUDA 12.1
+- Python 3.11 recommended
+
+## Model Sizes
+
+| Variant | Depths | Parameters |
+|---------|--------|------------|
+| S (Small) | [2, 4, 8, 4, 2] | ~2M |
+| M (Medium) | [8, 12, 16, 12, 8] | ~7M |
+| L (Large) | [8, 16, 32, 16, 8] | ~14M |
+
+## Citation
+
+If you find this work useful, please cite our paper:
+
+```bibtex
+@INPROCEEDINGS{11228727,
+  author={Li, Huibin and Liu, Haoran and Liu, Mingzhe and Xiao, Yulong and Li, Peng and Zan, Guibin},
+  booktitle={2025 International Joint Conference on Neural Networks (IJCNN)}, 
+  title={U-Net-Like Spiking Neural Networks for Single Image Dehazing}, 
+  year={2025},
+  pages={1-9},
+  doi={10.1109/IJCNN64981.2025.11228727}
+}
+```
+
+## License
+
+This project is released under the MIT License.
+
+## Links
+
+- [GitHub Repository](https://github.com/FengShaner/DehazeSNN)
+- [Paper (IEEE IJCNN 2025)](https://doi.org/10.1109/IJCNN64981.2025.11228727)
+- [Pretrained Models & Results (Zenodo)](https://doi.org/10.5281/zenodo.15486831)
+

config.json

@@ -0,0 +1,4 @@
+{
+  "dataset": "RESIDE-ITS",
+  "variant": "L"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:30de1401c53dc52c1fc40b3860fdac025d3313f4f4d3be436b0dab5204d5dd21
+size 19270012