Update README.md

README.md

@@ -1,3 +1,83 @@
 ---
 license: mit
+language:
+- en
+tags:
+- uncertainty
+- uncertainty-quantification
+- code
 ---
+
+# SPC-UQ: A Post-hoc, Efficient, and Unified Uncertainty Quantification Framework
+
+This repository contains the official code for **SPC-UQ** (Split-Point Consistency for Uncertainty Quantification), a post-hoc framework that jointly quantifies aleatoric and epistemic uncertainty with a single forward pass.
+
+It accompanies the paper:
+
+**"Post-Hoc Split-Point Self-Consistency Verification for Efficient, Unified Quantification of Aleatoric and Epistemic Uncertainty in Deep Learning."**
+
+## Key Features
+
+- **Post-hoc** – augments pre-trained network without architectural changes and retraining.
+- **Unified** – supports both regression and classification tasks in deep learning.  
+- **Efficient** – produces aleatoric and epistemic uncertainty estimates in one forward pass.
+- **Calibration** – provides mechanisms to calibrate aleatoric uncertainty and improve predictive reliability. 
+
+## Repository Structure
+
+```
+Cubic_Regression/             # Toy cubic regression for fast demonstration.
+MNIST_Classification/         # Digit classification for fast demonstration.
+UCI_Benchmarks/               # Standard UCI regression datasets for scalar regression evaluation.
+Monocular_Depth_Estimation/   # Monocular end-to-end image depth estimation for high-dimensional regression.
+Image_Classification/         # CIFAR-10/100, ImageNet-1K for large-scale image classification.
+Multimodal_Classification/    # LUMA multimodal benchmark (image/audio/text) for multimodal classification tasks.
+```
+
+Each directory provides scripts to reproduce the corresponding experiments.
+
+## Installation
+
+We recommend using [conda](https://docs.conda.io/en/latest/) to manage dependencies.  
+All required packages and versions except Multimodal_Classification are specified in `environment.yml`.
+
+### Step 1: Clone the repository
+```bash
+git clone https://huggingface.co/zzz0527/SPC-UQ
+cd SPC-UQ
+```
+### Step 2: Create and activate the environment
+```bash
+conda env create -f environment.yml
+conda activate spc_uq
+```
+
+## Usage
+
+Each subdirectory corresponds to a specific benchmark.  
+To run an experiment, navigate into the corresponding folder and follow the instructions provided in its `README.md`.  
+
+### Quick Start
+For a fast verification, we provide two lightweight benchmark tasks:
+
+```bash
+# Synthetic cubic regression
+python Cubic_Regression/run_cubic_tests.py
+
+# MNIST classification
+python MNIST_Classification/run_cls_tests.py
+```
+
+See the documentation in each subdirectory for details on dataset preparation, configuration options, and advanced usage.
+
+## Citation
+
+If you use SPC-UQ in your research, please cite our paper:
+
+```
+@article{zhao2025spc,
+  title   = {Post-Hoc Split-Point Self-Consistency Verification for Efficient, Unified Quantification of Aleatoric and Epistemic Uncertainty in Deep Learning},
+  author  = {Zhao, ZZ and Chen, Ke},
+  year    = {2025}
+}
+```