Improve dataset card: Add task category, sample usage, model details, and HF Hub link

This PR enhances the dataset card by:
- Adding `task_categories: - other` to the metadata for better discoverability.
- Adding a prominent link to the dataset's own Hugging Face Hub page (`https://huggingface.co/datasets/kyegorov/mcd_rppg`), which serves as the project page.
- Incorporating details about the "Fast Baseline Model" and its architecture from the GitHub README.
- Adding a "Sample Usage" section with code snippets for environment setup and running experiments, directly from the GitHub repository's `README.md`.
- Including the "Results and Comparison" tables from the GitHub README to provide performance benchmarks for the associated model.

The existing arXiv paper link (`https://arxiv.org/abs/2508.17924v1`) has been retained, as per the instructions not to replace an existing arXiv link with a Hugging Face Papers page link.

README.md

@@ -1,16 +1,21 @@
 ---
 license: cc-by-4.0
+size_categories:
+- 1K<n<10K
 tags:
 - medical
 - video
 - ecg
 - ppg
-size_categories:
-- 1K<n<10K
+task_categories:
+- other
 ---
+
 # MCD-rPPG: Multi-Camera Dataset for Remote Photoplethysmography
 
-This repository contains the dataset from the paper ["Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation"](https://arxiv.org/abs/2508.17924v1)
+This repository contains the dataset from the paper ["Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation"](https://arxiv.org/abs/2508.17924v1).
+
+The MCD-rPPG dataset is available on the Hugging Face Hub: [**MCD-rPPG Dataset**](https://huggingface.co/datasets/kyegorov/mcd_rppg)
 
 The presented large-scale multimodal MCD-rPPG dataset is designed for remote photoplethysmography (rPPG) and health biomarker estimation from video. The dataset includes synchronized video recordings from three cameras at different angles, PPG and ECG signals, and extended health metrics (arterial blood pressure, oxygen saturation, stress level, etc.) for 600 subjects in both resting and post-exercise states.
 
@@ -24,10 +29,58 @@ The dataset contains:
 *   **13 health biomarkers**: systolic/diastolic pressure, oxygen saturation, temperature, glucose, glycated hemoglobin, cholesterol, respiratory rate, arterial stiffness, stress level (PSM-25), age, sex, BMI.
 *   **Multi-view videos**: frontal webcam, FullHD camcorder, mobile phone camera.
 *   
-## Code and usage
+## Fast Baseline Model
+
+We propose an efficient multi-task model that:
+*   Processes video in **real-time on a CPU** (up to 13% faster than leading models).
+*   Estimates the **PPG signal** and **10+ health biomarkers** simultaneously.
+*   Is lightweight (~4 MB) and uses domain-specific preprocessing suitable for low-power devices.
+
+The model architecture combines domain-specific preprocessing (ROI selection on the face) with a convolutional network (1D Feature Pyramid Network).
+
+## Code and Sample Usage
 
 See GitHub repository [https://github.com/ksyegorov/mcd_rppg](https://github.com/ksyegorov/mcd_rppg)
 
+To get started with the code and reproduce experiments, follow these steps:
+
+1.  **Clone the repository:**
+    ```bash
+    git clone https://github.com/ksyegorov/mcd_rppg.git
+    cd mcd_rppg/
+    ```
+
+2.  **Install dependencies.** Using a virtual environment is recommended.
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+3.  **Run the notebooks** you are interested in (e.g., `train_SCNN_8roi_mcd_rppg.ipynb`) for training or reproducing experiments. Remember to download the MCD-rPPG dataset first.
+
+## Results and Comparison
+
+The tables below show key results of our model (Ours) compared to state-of-the-art (SOTA) alternatives. MAE (Mean Absolute Error) is calculated for the PPG signal and Heart Rate (HR).
+
+**Table: Model performance comparison (MAE) in cross-dataset scenarios**
+*(Summary of results from the paper)*
+
+| Model          | ... | MCD-rPPG (HR MAE) | ... |
+|----------------|-----|-------------------|-----|
+| PBV            | ... | 15.37             | ... |
+| OMIT           | ... | 4.78              | ... |
+| POS            | ... | 3.80              | ... |
+| PhysFormer     | ... | 4.08              | ... |
+| **Ours**       | ... | **4.86**          | ... |
+
+**Table: Performance for different camera views and inference speed**
+
+| Model          | CPU Inference (s) | Size (Mb) | Frontal PPG MAE | Side PPG MAE |
+|----------------|-------------------|-----------|-----------------|--------------|
+| POS            | 0.26              | 0         | 0.87            | 1.25         |
+| PhysFormer     | 0.93              | 28.4      | 0.46            | 0.97         |
+| **Ours**       | **0.15**          | **3.9**   | 0.68            | 1.10         |
+
+Complete results, including biomarker evaluation, are presented in the paper.
 
 ## Citation
 
@@ -40,3 +93,4 @@ If you use the MCD-rPPG dataset or code from this repository, please cite our wo
   journal={arXiv preprint arXiv:2508.17924},
   year={2024}
 }
+```