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README.md

@@ -13,13 +13,12 @@ tags:
 
 # IFNet
 
-IFNetV2 from Wang J et al (2023) .
+IFNetV2 from Wang J et al (2023) [ifnet].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** 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.
+> distributed here.** Instantiate the model and train it on your own
+> data.
 
 ## Quick start
 
@@ -38,163 +37,44 @@ model = IFNet(
 )
 ```
 
-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.IFNet.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.IFNet.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/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:**
+## Architecture
 
- .. code::
-     from braindecode.models import IFNet
+![IFNet architecture](https://raw.githubusercontent.com/Jiaheng-Wang/IFNet/main/IFNet.png)
 
-     # 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",
-     )
+## Parameters
 
- **Loading a model from the Hub:**
+| Parameter | Type | Description |
+|---|---|---|
+| `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. |
 
- .. code::
-     from braindecode.models import IFNet
 
-     # Load pretrained model
-     model = IFNet.from_pretrained("username/my-ifnet-model")
+## References
 
-     # 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)
+1. 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.
+2. 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
 
-     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:
+Cite the original architecture paper (see *References* above) and braindecode:
 
 ```bibtex
 @article{aristimunha2025braindecode,