Replace with clean markdown card

README.md

@@ -13,13 +13,12 @@ tags:
 
 # EEGITNet
 
-EEG-ITNet from Salami, et al (2022)
+EEG-ITNet from Salami, et al (2022) [Salami2022]
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.EEGITNet` 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
 
@@ -38,148 +37,44 @@ model = EEGITNet(
 )
 ```
 
-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.EEGITNet.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.EEGITNet.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/eegitnet.py#L12>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>EEG-ITNet from Salami, et al (2022) [Salami2022]_</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><span style="display:inline-block;padding:2px 8px;border-radius:4px;background:#6c757d;color:white;font-size:11px;font-weight:600;margin-right:4px;">Recurrent</span>
-
-
-
- .. figure:: https://braindecode.org/dev/_static/model/eegitnet.jpg
-     :align: center
-     :alt: EEG-ITNet Architecture
-
- EEG-ITNet: An Explainable Inception Temporal
- Convolutional Network for motor imagery classification from
- Salami et al. 2022.
-
- See [Salami2022]_ for details.
-
- Code adapted from https://github.com/abbassalami/eeg-itnet
-
- Parameters
- ----------
- drop_prob: float
-     Dropout probability.
- 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``.
- kernel_length : int, optional
-     Kernel length for inception branches. Determines the temporal receptive field.
-     Default is 16.
- pool_kernel : int, optional
-     Pooling kernel size for the average pooling layer. Default is 4.
- tcn_in_channel : int, optional
-     Number of input channels for Temporal Convolutional (TC) blocks. Default is 14.
- tcn_kernel_size : int, optional
-     Kernel size for the TC blocks. Determines the temporal receptive field.
-     Default is 4.
- tcn_padding : int, optional
-     Padding size for the TC blocks to maintain the input dimensions. Default is 3.
- drop_prob : float, optional
-     Dropout probability applied after certain layers to prevent overfitting.
-     Default is 0.4.
- tcn_dilatation : int, optional
-     Dilation rate for the first TC block. Subsequent blocks will have
-     dilation rates multiplied by powers of 2. Default is 1.
-
- Notes
- -----
- This implementation is not guaranteed to be correct, has not been checked
- by original authors, only reimplemented from the paper based on author implementation.
-
-
- References
- ----------
- .. [Salami2022] A. Salami, J. Andreu-Perez and H. Gillmeister, "EEG-ITNet:
-     An Explainable Inception Temporal Convolutional Network for motor
-     imagery classification," in IEEE Access,
-     doi: 10.1109/ACCESS.2022.3161489.
-
- .. 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 EEGITNet
-
-     # Train your model
-     model = EEGITNet(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-eegitnet-model",
-         commit_message="Initial model upload",
-     )
-
- **Loading a model from the Hub:**
-
- .. code::
-     from braindecode.models import EEGITNet
-
-     # Load pretrained model
-     model = EEGITNet.from_pretrained("username/my-eegitnet-model")
-
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = EEGITNet.from_pretrained("username/my-eegitnet-model", n_outputs=4)
 
- **Extracting features and replacing the head:**
+## Architecture
 
- .. code::
-     import torch
+![EEGITNet architecture](https://braindecode.org/dev/_static/model/eegitnet.jpg)
 
-     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)
+## Parameters
 
- **Saving and restoring full configuration:**
+| Parameter | Type | Description |
+|---|---|---|
+| `drop_prob: float` | — | Dropout probability. |
+| `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`. |
+| `kernel_length` | int, optional | Kernel length for inception branches. Determines the temporal receptive field. Default is 16. |
+| `pool_kernel` | int, optional | Pooling kernel size for the average pooling layer. Default is 4. |
+| `tcn_in_channel` | int, optional | Number of input channels for Temporal Convolutional (TC) blocks. Default is 14. |
+| `tcn_kernel_size` | int, optional | Kernel size for the TC blocks. Determines the temporal receptive field. Default is 4. |
+| `tcn_padding` | int, optional | Padding size for the TC blocks to maintain the input dimensions. Default is 3. |
+| `drop_prob` | float, optional | Dropout probability applied after certain layers to prevent overfitting. Default is 0.4. |
+| `tcn_dilatation` | int, optional | Dilation rate for the first TC block. Subsequent blocks will have dilation rates multiplied by powers of 2. Default is 1. |
 
- .. code::
-     import json
 
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
+## References
 
-     model2 = EEGITNet.from_config(config)    # reconstruct (no weights)
+1. A. Salami, J. Andreu-Perez and H. Gillmeister, "EEG-ITNet: An Explainable Inception Temporal Convolutional Network for motor imagery classification," in IEEE Access, doi: 10.1109/ACCESS.2022.3161489.
 
- 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.
-
- 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,