Add pipeline tag and improve model card

README.md

@@ -1,30 +1,29 @@
 ---
-license: mit
 library_name: diffusers
+license: mit
+pipeline_tag: image-to-image
 tags:
-  - computed-tomography
-  - ct-reconstruction
-  - diffusion-model
-  - inverse-problems
-  - dm4ct
-  - sparse-view-ct
+- computed-tomography
+- ct-reconstruction
+- diffusion-model
+- inverse-problems
+- dm4ct
+- sparse-view-ct
 ---
 
 # Pixel Diffusion UNet – LoDoChallenge (DM4CT)
 
-This repository contains the pretrained **pixel-space diffusion UNet** used in the  
-**DM4CT: Benchmarking Diffusion Models for CT Reconstruction (ICLR 2026)** benchmark.
+This repository contains the pretrained **pixel-space diffusion UNet** presented in the paper [DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction](https://huggingface.co/papers/2602.18589) (ICLR 2026).
 
-🔗 Paper: https://openreview.net/forum?id=YE5scJekg5  
-🔗 Arxiv: https://arxiv.org/abs/2602.18589  
-🔗 Codebase: https://github.com/DM4CT/DM4CT  
+- **Project Page:** [https://dm4ct.github.io/DM4CT/](https://dm4ct.github.io/DM4CT/)
+- **Codebase:** [https://github.com/DM4CT/DM4CT](https://github.com/DM4CT/DM4CT)
+- **Paper:** [DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction](https://huggingface.co/papers/2602.18589)
 
 ---
 
 ## 🔬 Model Overview
 
-This model learns a **prior over CT reconstruction images** using a denoising diffusion probabilistic model (DDPM).  
-It operates directly in **pixel space** (not latent space).
+This model learns a **prior over CT reconstruction images** using a denoising diffusion probabilistic model (DDPM). It operates directly in **pixel space** (not latent space).
 
 - **Architecture**: 2D UNet (Diffusers `UNet2DModel`)
 - **Input resolution**: 512 × 512
@@ -34,42 +33,58 @@ It operates directly in **pixel space** (not latent space).
 - **Training dataset**: Low Dose Grand Challenge (LoDoChallenge)
 - **Intensity normalization**: Rescaled to (-1, 1)
 
-This model is intended to be combined with data-consistency correction for CT reconstruction.
+This model is intended to be combined with data-consistency correction for CT reconstruction tasks as detailed in the DM4CT benchmark.
 
 ---
 
 ## 📊 Dataset: Low Dose Grand Challenge
 
-Source:
-https://www.aapm.org/grandchallenge/lowdosect/
+Source: [AAPM Low Dose CT Grand Challenge](https://www.aapm.org/grandchallenge/lowdosect/)
 
 Preprocessing steps:
-- Train/test split
-- Rescale reconstructed slices to (-1, 1)
-- No geometry information is embedded in the model
+- Train/test split.
+- Rescale reconstructed slices to (-1, 1).
+- No geometry information is embedded in the model.
 
-The model learns an unconditional image prior over CT slices.
+The model learns an unconditional image prior over medical CT slices.
 
 ---
 
 ## 🧠 Training Details
 
-- Optimizer: AdamW
-- Learning rate: 1e-4
-- Batch size: (insert your batch size)
-- Training steps: (insert number of steps)
-- Hardware: NVIDIA A100 GPU
-
-Training script:
-https://github.com/DM4CT/DM4CT/blob/main/train_pixel.py
+- **Optimizer**: AdamW
+- **Learning rate**: 1e-4
+- **Hardware**: NVIDIA A100 GPU
+- **Training script**: [train_pixel.py](https://github.com/DM4CT/DM4CT/blob/main/train_pixel.py)
 
 ---
 
 ## 🚀 Usage
 
+You can use this model with the `diffusers` library:
+
 ```python
 from diffusers import DDPMPipeline
+
+# Load the pretrained pipeline
 pipeline = DDPMPipeline.from_pretrained("jiayangshi/lodochallenge_pixel_diffusion")
-)
 
-model.eval()
+# Generate a sample (unconditional CT slice prior)
+image = pipeline().images[0]
+image.save("generated_ct_slice.png")
+```
+
+---
+
+## Citation
+
+```bibtex
+@inproceedings{
+  shi2026dmct,
+  title={{DM}4{CT}: Benchmarking Diffusion Models for Computed Tomography Reconstruction},
+  author={Shi, Jiayang and Pelt, Dani{\"e}l M and Batenburg, K Joost},
+  booktitle={The Fourteenth International Conference on Learning Representations},
+  year={2026},
+  url={https://openreview.net/forum?id=YE5scJekg5}
+}
+```