Add architecture-only model card

README.md

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+---
+license: bsd-3-clause
+library_name: braindecode
+pipeline_tag: feature-extraction
+tags:
+  - eeg
+  - biosignal
+  - pytorch
+  - neuroscience
+  - braindecode
+  - convolutional
+---
+
+# IFNet
+
+IFNetV2 from Wang J et al (2023) .
+
+> **Architecture-only repository.** This repo documents the
+> `braindecode.models.IFNet` 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.
+
+## Quick start
+
+```bash
+pip install braindecode
+```
+
+```python
+from braindecode.models import IFNet
+
+model = IFNet(
+    n_chans=22,
+    sfreq=250,
+    input_window_seconds=4.0,
+    n_outputs=4,
+)
+```
+
+The signal-shape arguments above are example defaults — adjust them
+to match your recording.
+
+## Documentation
+
+- Full API reference (parameters, references, architecture figure):
+  <https://braindecode.org/stable/generated/braindecode.models.IFNet.html>
+- Interactive browser with live instantiation:
+  <https://huggingface.co/spaces/braindecode/model-explorer>
+- Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/ifnet.py#L31>
+
+## Architecture description
+
+The block below is the rendered class docstring (parameters,
+references, architecture figure where available).
+
+<div class='bd-doc'><main>
+<p>IFNetV2 from Wang J et al (2023) [ifnet]_.</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:#0072B2;color:white;font-size:11px;font-weight:600;margin-right:4px;">Filterbank</span>
+
+
+
+ .. figure:: https://raw.githubusercontent.com/Jiaheng-Wang/IFNet/main/IFNet.png
+     :align: center
+     :alt: IFNetV2 Architecture
+
+ Overview of the Interactive Frequency Convolutional Neural Network architecture.
+
+ IFNetV2 is designed to effectively capture spectro-spatial-temporal
+ features for motor imagery decoding from EEG data. The model consists of
+ three stages: Spectro-Spatial Feature Representation, Cross-Frequency
+ Interactions, and Classification.
+
+ - **Spectro-Spatial Feature Representation**: The raw EEG signals are
+   filtered into two characteristic frequency bands: low (4-16 Hz) and
+   high (16-40 Hz), covering the most relevant motor imagery bands.
+   Spectro-spatial features are then extracted through 1D point-wise
+   spatial convolution followed by temporal convolution.
+
+ - **Cross-Frequency Interactions**: The extracted spectro-spatial
+   features from each frequency band are combined through an element-wise
+   summation operation, which enhances feature representation while
+   preserving distinct characteristics.
+
+ - **Classification**: The aggregated spectro-spatial features are further
+   reduced through temporal average pooling and passed through a fully
+   connected layer followed by a softmax operation to generate output
+   probabilities for each class.
+
+ Notes
+ -----
+ This implementation is not guaranteed to be correct, has not been checked
+ by original authors, only reimplemented from the paper description and
+ Torch source code [ifnetv2code]_. Version 2 is present only in the repository,
+ and the main difference is one pooling layer, describe at the TABLE VII
+ from the paper: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10070810
+
+
+ Parameters
+ ----------
+ bands : list[tuple[int, int]] or int or None, default=[[4, 16], (16, 40)]
+     Frequency bands for filtering.
+ out_planes : int, default=64
+     Number of output feature dimensions.
+ kernel_sizes : tuple of int, default=(63, 31)
+     List of kernel sizes for temporal convolutions.
+ patch_size : int, default=125
+     Size of the patches for temporal segmentation.
+ drop_prob : float, default=0.5
+     Dropout probability.
+ activation : nn.Module, default=nn.GELU
+     Activation function after the InterFrequency Layer.
+ verbose : bool, default=False
+     Verbose to control the filtering layer
+ filter_parameters : dict, default={}
+     Additional parameters for the filter bank layer.
+
+ References
+ ----------
+ .. [ifnet] Wang, J., Yao, L., & Wang, Y. (2023). IFNet: An interactive
+     frequency convolutional neural network for enhancing motor imagery
+     decoding from EEG. IEEE Transactions on Neural Systems and
+     Rehabilitation Engineering, 31, 1900-1911.
+ .. [ifnetv2code] Wang, J., Yao, L., & Wang, Y. (2023). IFNet: An interactive
+     frequency convolutional neural network for enhancing motor imagery
+     decoding from EEG.
+     https://github.com/Jiaheng-Wang/IFNet
+
+ .. 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 IFNet
+
+     # Train your model
+     model = IFNet(n_chans=22, n_outputs=4, n_times=1000)
+     # ... training code ...
+
+     # Push to the Hub
+     model.push_to_hub(
+         repo_id="username/my-ifnet-model",
+         commit_message="Initial model upload",
+     )
+
+ **Loading a model from the Hub:**
+
+ .. code::
+     from braindecode.models import IFNet
+
+     # Load pretrained model
+     model = IFNet.from_pretrained("username/my-ifnet-model")
+
+     # Load with a different number of outputs (head is rebuilt automatically)
+     model = IFNet.from_pretrained("username/my-ifnet-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:**
+
+ .. code::
+     import json
+
+     config = model.get_config()            # all __init__ params
+     with open("config.json", "w") as f:
+         json.dump(config, f)
+
+     model2 = IFNet.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:
+
+```bibtex
+@article{aristimunha2025braindecode,
+  title   = {Braindecode: a deep learning library for raw electrophysiological data},
+  author  = {Aristimunha, Bruno and others},
+  journal = {Zenodo},
+  year    = {2025},
+  doi     = {10.5281/zenodo.17699192},
+}
+```
+
+## License
+
+BSD-3-Clause for the model code (matching braindecode).
+Pretraining-derived weights, if you fine-tune from a checkpoint,
+inherit the licence of that checkpoint and its training corpus.