Update README.rst

Author: loki-veera

README.rst

@@ -19,10 +19,60 @@ Gall <https://pages.iai.uni-bonn.de/gall_juergen/>`__\ :sup:`1,3`
 **Abstract:** Modern methods for fine-tuning a Vision Transformer (ViT) like Low-Rank Adaptation (LoRA) and its variants demonstrate impressive performance. However, these methods ignore the high-dimensional nature of Multi-Head Attention (MHA) weight tensors. To address this limitation, we propose Canonical Rank Adaptation (CaRA). CaRA leverages tensor mathematics, first by tensorising the transformer into two different tensors; one for projection layers in MHA and the other for feed-forward layers. Second, the tensorised formulation is fine-tuned using the low-rank adaptation in Canonical-Polyadic Decomposition (CPD) form. Employing CaRA efficiently minimizes the number of trainable parameters. Experimentally, CaRA outperforms existing Parameter-Efficient Fine-Tuning (PEFT) methods in visual classification benchmarks such as Visual Task Adaptation Benchmark (VTAB)-1k and Fine-Grained Visual Categorization (FGVC).
 
 
-Note
-****
-We are commited to providing thoroughly tested and well-packaged code.
-The code will be soon released once the process is completed. 
+.. image:: https://raw.githubusercontent.com/BonnBytes/CaRA/refs/heads/dev/images/tensorisation.jpg
+   :width: 100%
+   :alt: Alternative text
+
+
+Installation
+============
+
+Use `UV <https://docs.astral.sh/uv/>`_ to install the requirements
+
+For CPU based pytorch
+
+.. code:: bash
+
+   uv sync --extra cpu 
+
+For CUDA based pytorch
+
+.. code:: bash
+
+   uv sync --extra cu118
+
+
+Datasets
+=======
+
+In the case of VTAB-1k benchmark, refer to the dataset download instructions from `NOAH <https://github.com/ZhangYuanhan-AI/NOAH>`_. We download the datasets for FGVC benchmark from their respective sources.
+
+Note: Create a ``data`` folder in the root and place the datasets inside this folder.
+
+
+Pretrained models
+=================
+Please refer to the download links provided in the paper.
+
+
+Training
+========
+For fine-tuning ViT use the following command.
+
+.. code:: bash
+   
+   export PYTHONPATH=.
+   python image_classification/vit_cp.py --dataset=<choice_of_dataset> --dim=<rank>
+
+
+Evaluation
+==========
+We provide the link for fine-tuned models for each dataset in VTAB-1k benchmark `here <https://uni-bonn.sciebo.de/s/YAtcRDHxdwnBGq7>`_. To reproduce results from the paper, download the model and execute the following command
+
+.. code:: bash
+
+   export PYTHONPATH=.
+   python image_classification/vit_cp.py --dataset=<choice_of_dataset> --dim=<rank> --evaluate=<path_to_model>
 
 
 Acknowledgments
@@ -38,4 +88,4 @@ The code is built on the implementation of `FacT <https://github.com/JieShibo/PE
    :alt: Project Page
 .. |Arxiv| image:: https://img.shields.io/badge/OpenReview-Paper-blue
    :target: https://openreview.net/pdf?id=vexHifrbJg
-   :alt: Paper
+   :alt: Paper