Improve model card: add pipeline tag, library name, and link to code

README.md

@@ -1,12 +1,55 @@
 ---
-license: mit
+base_model:
+- microsoft/resnet-50
 datasets:
 - wltjr1007/DomainNet
 language:
 - en
+license: mit
 metrics:
 - accuracy
-base_model:
-- microsoft/resnet-50
+pipeline_tag: image-classification
+library_name: pytorch
 ---
-arxiv.org/abs/2505.24216
+
+arxiv.org/abs/2505.24216
+
+[Code](https://github.com/PrasannaPulakurthi/SPM)
+
+# Shuffle PatchMix
+
+This is the official implementation of the **ICIP 2025** paper **"Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation"**, by [Prasanna Reddy Pulakurthi](https://www.prasannapulakurthi.com/), [Majid Rabbani](https://www.rit.edu/directory/mxreee-majid-rabbani), [Jamison Heard](https://www.rit.edu/directory/jrheee-jamison-heard), [Sohail A. Dianat](https://www.rit.edu/directory/sadeee-sohail-dianat), [Celso M. de Melo](https://celsodemelo.net/), and [Raghuveer Rao](https://ieeexplore.ieee.org/author/37281258600).
+
+Shuffle PatchMix (SPM) is an augmentation technique that shuffles and blends image patches to generate diverse and challenging augmentations. It is combined with a novel reweighting strategy that prioritizes reliable pseudo-labels to mitigate label noise.
+
+## Installation
+
+1.  Clone this repository.
+
+```bash
+git clone https://github.com/PrasannaPulakurthi/SPM.git
+cd SPM
+```
+
+2.  Install requirements using Python 3.9.
+
+```bash
+conda create -n spm-env python=3.9
+conda activate spm-env
+```
+
+3.  The code is tested with [Pytorch](https://pytorch.org/get-started/locally/) 1.7.1, CUDA 11.0.
+
+```bash
+pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 -f https://download.pytorch.org/whl/torch_stable.html
+```
+
+4.  Please also make sure to install additional packages using the following command.
+
+```bash
+pip install -r requirements.txt
+```
+
+## Datasets
+
+The model was evaluated on three major benchmarks: PACS, VisDA-C, and DomainNet-126. Instructions for preparing these datasets can be found in the Github repository.