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README.md

@@ -13,13 +13,12 @@ tags:
 
 # TIDNet
 
-Thinker Invariance DenseNet model from Kostas et al (2020) .
+Thinker Invariance DenseNet model from Kostas et al (2020) [TIDNet].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.TIDNet` 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
 
@@ -38,149 +37,48 @@ model = TIDNet(
 )
 ```
 
-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.TIDNet.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.TIDNet.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/tidnet.py#L13>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>Thinker Invariance DenseNet model from Kostas et al (2020) [TIDNet]_.</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>
-
-
-
- .. figure:: https://content.cld.iop.org/journals/1741-2552/17/5/056008/revision3/jneabb7a7f1_hr.jpg
-     :align: center
-     :alt: TIDNet Architecture
-
- See [TIDNet]_ for details.
-
- Parameters
- ----------
- s_growth : int
-     DenseNet-style growth factor (added filters per DenseFilter)
- t_filters : int
-     Number of temporal filters.
- drop_prob : float
-     Dropout probability
- pooling : int
-     Max temporal pooling (width and stride)
- temp_layers : int
-     Number of temporal layers
- spat_layers : int
-     Number of DenseFilters
- temp_span : float
-     Percentage of n_times that defines the temporal filter length:
-     temp_len = ceil(temp_span * n_times)
-     e.g A value of 0.05 for temp_span with 1500 n_times will yield a temporal
-     filter of length 75.
- bottleneck : int
-     Bottleneck factor within Densefilter
- summary : int
-     Output size of AdaptiveAvgPool1D layer. If set to -1, value will be calculated
-     automatically (n_times // pooling).
- in_chans :
-     Alias for n_chans.
- n_classes:
-     Alias for n_outputs.
- input_window_samples :
-     Alias for n_times.
- activation: nn.Module, default=nn.LeakyReLU
-     Activation function class to apply. Should be a PyTorch activation
-     module class like ``nn.ReLU`` or ``nn.ELU``. Default is ``nn.LeakyReLU``.
-
- Notes
- -----
- Code adapted from: https://github.com/SPOClab-ca/ThinkerInvariance/
-
- References
- ----------
- .. [TIDNet] Kostas, D. & Rudzicz, F.
-     Thinker invariance: enabling deep neural networks for BCI across more
-     people.
-     J. Neural Eng. 17, 056008 (2020).
-     doi: 10.1088/1741-2552/abb7a7.
-
- .. 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 TIDNet
 
-     # Train your model
-     model = TIDNet(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
+## Architecture
 
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-tidnet-model",
-         commit_message="Initial model upload",
-     )
+![TIDNet architecture](https://content.cld.iop.org/journals/1741-2552/17/5/056008/revision3/jneabb7a7f1_hr.jpg)
 
- **Loading a model from the Hub:**
 
- .. code::
-     from braindecode.models import TIDNet
+## Parameters
 
-     # Load pretrained model
-     model = TIDNet.from_pretrained("username/my-tidnet-model")
+| Parameter | Type | Description |
+|---|---|---|
+| `s_growth` | int | DenseNet-style growth factor (added filters per DenseFilter) |
+| `t_filters` | int | Number of temporal filters. |
+| `drop_prob` | float | Dropout probability |
+| `pooling` | int | Max temporal pooling (width and stride) |
+| `temp_layers` | int | Number of temporal layers |
+| `spat_layers` | int | Number of DenseFilters |
+| `temp_span` | float | Percentage of n_times that defines the temporal filter length: temp_len = ceil(temp_span * n_times) e.g A value of 0.05 for temp_span with 1500 n_times will yield a temporal filter of length 75. |
+| `bottleneck` | int | Bottleneck factor within Densefilter |
+| `summary` | int | Output size of AdaptiveAvgPool1D layer. If set to -1, value will be calculated automatically (n_times // pooling). |
+| `in_chans` | — | Alias for n_chans. |
+| `n_classes:` | — | Alias for n_outputs. |
+| `input_window_samples` | — | Alias for n_times. |
+| `activation: nn.Module, default=nn.LeakyReLU` | — | Activation function class to apply. Should be a PyTorch activation module class like `nn.ReLU` or `nn.ELU`. Default is `nn.LeakyReLU`. |
 
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = TIDNet.from_pretrained("username/my-tidnet-model", n_outputs=4)
 
- **Extracting features and replacing the head:**
+## References
 
- .. code::
-     import torch
+1. Kostas, D. & Rudzicz, F. Thinker invariance: enabling deep neural networks for BCI across more people. J. Neural Eng. 17, 056008 (2020). doi: 10.1088/1741-2552/abb7a7.
 
-     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:**
-
- .. code::
-     import json
-
-     config = model.get_config()            # all __init__ params
-     with open("config.json", "w") as f:
-         json.dump(config, f)
-
-     model2 = TIDNet.from_config(config)    # reconstruct (no weights)
-
- 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,