Replace with clean markdown card

README.md

@@ -9,18 +9,16 @@ tags:
   - neuroscience
   - braindecode
   - convolutional
-  - transformer
 ---
 
 # SPARCNet
 
-Seizures, Periodic and Rhythmic pattern Continuum Neural Network (SPaRCNet) from Jing et al (2023) .
+Seizures, Periodic and Rhythmic pattern Continuum Neural Network (SPaRCNet) from Jing et al (2023) [jing2023].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.SPARCNet` class. **No pretrained weights are
-> distributed here** — instantiate the model and train it on your own
-> data, or fine-tune from a published foundation-model checkpoint
-> separately.
+> distributed here.** Instantiate the model and train it on your own
+> data.
 
 ## Quick start
 
@@ -39,137 +37,39 @@ model = SPARCNet(
 )
 ```
 
-The signal-shape arguments above are example defaults — adjust them
-to match your recording.
+The signal-shape arguments above are illustrative defaults — adjust to
+match your recording.
 
 ## Documentation
-
-- Full API reference (parameters, references, architecture figure):
-  <https://braindecode.org/stable/generated/braindecode.models.SPARCNet.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.SPARCNet.html>
+- Interactive browser (live instantiation, parameter counts):
   <https://huggingface.co/spaces/braindecode/model-explorer>
 - Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/sparcnet.py#L13>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>Seizures, Periodic and Rhythmic pattern Continuum Neural Network (SPaRCNet) from Jing et al (2023) [jing2023]_.</p>
-<span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#5cb85c;color:white;font-size:11px;font-weight:600;margin-right:4px;">Convolution</span>
-
-
-
- This is a temporal CNN model for biosignal classification based on the DenseNet
- architecture.
-
- The model is based on the unofficial implementation [Code2023]_.
-
- .. versionadded:: 0.9
-
- Notes
- -----
- This implementation is not guaranteed to be correct, has not been checked
- by original authors.
-
- Parameters
- ----------
- block_layers : int, optional
-     Number of layers per dense block. Default is 4.
- growth_rate : int, optional
-     Growth rate of the DenseNet. Default is 16.
- bn_size : int, optional
-     Bottleneck size. Default is 16.
- drop_prob : float, optional
-     Dropout rate. Default is 0.5.
- conv_bias : bool, optional
-     Whether to use bias in convolutional layers. Default is True.
- batch_norm : bool, optional
-     Whether to use batch normalization. Default is True.
- activation: nn.Module, default=nn.ELU
-     Activation function class to apply. Should be a PyTorch activation
-     module class like ``nn.ReLU`` or ``nn.ELU``. Default is ``nn.ELU``.
-
- References
- ----------
- .. [jing2023] Jing, J., Ge, W., Hong, S., Fernandes, M. B., Lin, Z.,
-    Yang, C., ... & Westover, M. B. (2023). Development of expert-level
-    classification of seizures and rhythmic and periodic
-    patterns during eeg interpretation. Neurology, 100(17), e1750-e1762.
- .. [Code2023] Yang, C., Westover, M.B. and Sun, J., 2023. BIOT
-    Biosignal Transformer for Cross-data Learning in the Wild.
-    GitHub https://github.com/ycq091044/BIOT (accessed 2024-02-13)
-
- .. rubric:: Hugging Face Hub integration
-
- When the optional ``huggingface_hub`` package is installed, all models
- automatically gain the ability to be pushed to and loaded from the
- Hugging Face Hub. Install with::
-
-     pip install braindecode[hub]
-
- **Pushing a model to the Hub:**
-
- .. code::
-     from braindecode.models import SPARCNet
-
-     # Train your model
-     model = SPARCNet(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-sparcnet-model",
-         commit_message="Initial model upload",
-     )
-
- **Loading a model from the Hub:**
-
- .. code::
-     from braindecode.models import SPARCNet
-
-     # Load pretrained model
-     model = SPARCNet.from_pretrained("username/my-sparcnet-model")
-
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = SPARCNet.from_pretrained("username/my-sparcnet-model", n_outputs=4)
-
- **Extracting features and replacing the head:**
-
- .. code::
-     import torch
-
-     x = torch.randn(1, model.n_chans, model.n_times)
-     # Extract encoder features (consistent dict across all models)
-     out = model(x, return_features=True)
-     features = out["features"]
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
 
- **Saving and restoring full configuration:**
+## Parameters
 
- .. code::
-     import json
+| Parameter | Type | Description |
+|---|---|---|
+| `block_layers` | int, optional | Number of layers per dense block. Default is 4. |
+| `growth_rate` | int, optional | Growth rate of the DenseNet. Default is 16. |
+| `bn_size` | int, optional | Bottleneck size. Default is 16. |
+| `drop_prob` | float, optional | Dropout rate. Default is 0.5. |
+| `conv_bias` | bool, optional | Whether to use bias in convolutional layers. Default is True. |
+| `batch_norm` | bool, optional | Whether to use batch normalization. Default is True. |
+| `activation: nn.Module, default=nn.ELU` | — | Activation function class to apply. Should be a PyTorch activation module class like `nn.ReLU` or `nn.ELU`. Default is `nn.ELU`. |
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
 
-     model2 = SPARCNet.from_config(config)    # reconstruct (no weights)
+## References
 
- All model parameters (both EEG-specific and model-specific such as
- dropout rates, activation functions, number of filters) are automatically
- saved to the Hub and restored when loading.
+1. Jing, J., Ge, W., Hong, S., Fernandes, M. B., Lin, Z., Yang, C., ... & Westover, M. B. (2023). Development of expert-level classification of seizures and rhythmic and periodic patterns during eeg interpretation. Neurology, 100(17), e1750-e1762.
+2. Yang, C., Westover, M.B. and Sun, J., 2023. BIOT Biosignal Transformer for Cross-data Learning in the Wild. GitHub https://github.com/ycq091044/BIOT (accessed 2024-02-13)
 
- See :ref:`load-pretrained-models` for a complete tutorial.</main>
-</div>
 
 ## Citation
 
-Please cite both the original paper for this architecture (see the
-*References* section above) and braindecode:
+Cite the original architecture paper (see *References* above) and braindecode:
 
 ```bibtex
 @article{aristimunha2025braindecode,