Replace with clean markdown card

README.md

@@ -14,13 +14,12 @@ tags:
 
 # CTNet
 
-CTNet from Zhao, W et al (2024) .
+CTNet from Zhao, W et al (2024) [ctnet].
 
-> **Architecture-only repository.** This repo documents the
+> **Architecture-only repository.** Documents the
 > `braindecode.models.CTNet` 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
 
@@ -39,166 +38,47 @@ model = CTNet(
 )
 ```
 
-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.CTNet.html>
-- Interactive browser with live instantiation:
+- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.CTNet.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/ctnet.py#L27>
 
-## Architecture description
-
-The block below is the rendered class docstring (parameters,
-references, architecture figure where available).
-
-<div class='bd-doc'><main>
-<p>CTNet from Zhao, W et al (2024) [ctnet]_.</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:#56B4E9;color:white;font-size:11px;font-weight:600;margin-right:4px;">Attention/Transformer</span>
-
-
-
-  A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
-
-  .. figure:: https://raw.githubusercontent.com/snailpt/CTNet/main/architecture.png
-     :align: center
-     :alt: CTNet Architecture
-
- CTNet is an end-to-end neural network architecture designed for classifying motor imagery (MI) tasks from EEG signals.
- The model combines convolutional neural networks (CNNs) with a Transformer encoder to capture both local and global temporal dependencies in the EEG data.
-
- The architecture consists of three main components:
-
- 1. **Convolutional Module**:
-
-     - Apply :class:`EEGNet` to perform some feature extraction, denoted here as
-       _PatchEmbeddingEEGNet module.
-
- 2. **Transformer Encoder Module**:
-
-     - Utilizes multi-head self-attention mechanisms as EEGConformer but
-       with residual blocks.
-
- 3. **Classifier Module**:
-
-     - Combines features from both the convolutional module
-       and the Transformer encoder.
-     - Flattens the combined features and applies dropout for regularization.
-     - Uses a fully connected layer to produce the final classification output.
-
- Parameters
- ----------
- activation : nn.Module, default=nn.GELU
-     Activation function to use in the network.
- num_heads : int, default=4
-     Number of attention heads in the Transformer encoder.
- embed_dim : int or None, default=None
-     Embedding size (dimensionality) for the Transformer encoder.
- num_layers : int, default=6
-     Number of encoder layers in the Transformer.
- n_filters_time : int, default=20
-     Number of temporal filters in the first convolutional layer.
- kernel_size : int, default=64
-     Kernel size for the temporal convolutional layer.
- depth_multiplier : int, default=2
-     Multiplier for the number of depth-wise convolutional filters.
- pool_size_1 : int, default=8
-     Pooling size for the first average pooling layer.
- pool_size_2 : int, default=8
-     Pooling size for the second average pooling layer.
-     cnn_drop_prob: float, default=0.3
-     Dropout probability after convolutional layers.
- att_positional_drop_prob : float, default=0.1
-     Dropout probability for the positional encoding in the Transformer.
- final_drop_prob : float, default=0.5
-     Dropout probability before the final classification layer.
-
- Notes
- -----
- This implementation is adapted from the original CTNet source code
- [ctnetcode]_ to comply with Braindecode's model standards.
-
- References
- ----------
- .. [ctnet] Zhao, W., Jiang, X., Zhang, B., Xiao, S., & Weng, S. (2024).
-     CTNet: a convolutional transformer network for EEG-based motor imagery
-     classification. Scientific Reports, 14(1), 20237.
- .. [ctnetcode] Zhao, W., Jiang, X., Zhang, B., Xiao, S., & Weng, S. (2024).
-     CTNet source code:
-     https://github.com/snailpt/CTNet
-
- .. 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 CTNet
-
-     # Train your model
-     model = CTNet(n_chans=22, n_outputs=4, n_times=1000)
-     # ... training code ...
-
-     # Push to the Hub
-     model.push_to_hub(
-         repo_id="username/my-ctnet-model",
-         commit_message="Initial model upload",
-     )
 
- **Loading a model from the Hub:**
+## Architecture
 
- .. code::
-     from braindecode.models import CTNet
+![CTNet architecture](https://raw.githubusercontent.com/snailpt/CTNet/main/architecture.png)
 
-     # Load pretrained model
-     model = CTNet.from_pretrained("username/my-ctnet-model")
 
-     # Load with a different number of outputs (head is rebuilt automatically)
-     model = CTNet.from_pretrained("username/my-ctnet-model", n_outputs=4)
+## Parameters
 
- **Extracting features and replacing the head:**
+| Parameter | Type | Description |
+|---|---|---|
+| `activation` | nn.Module, default=nn.GELU | Activation function to use in the network. |
+| `num_heads` | int, default=4 | Number of attention heads in the Transformer encoder. |
+| `embed_dim` | int or None, default=None | Embedding size (dimensionality) for the Transformer encoder. |
+| `num_layers` | int, default=6 | Number of encoder layers in the Transformer. |
+| `n_filters_time` | int, default=20 | Number of temporal filters in the first convolutional layer. |
+| `kernel_size` | int, default=64 | Kernel size for the temporal convolutional layer. |
+| `depth_multiplier` | int, default=2 | Multiplier for the number of depth-wise convolutional filters. |
+| `pool_size_1` | int, default=8 | Pooling size for the first average pooling layer. |
+| `pool_size_2` | int, default=8 | Pooling size for the second average pooling layer. cnn_drop_prob: float, default=0.3 Dropout probability after convolutional layers. |
+| `att_positional_drop_prob` | float, default=0.1 | Dropout probability for the positional encoding in the Transformer. |
+| `final_drop_prob` | float, default=0.5 | Dropout probability before the final classification layer. |
 
- .. 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"]
+## References
 
-     # Replace the classification head
-     model.reset_head(n_outputs=10)
+1. Zhao, W., Jiang, X., Zhang, B., Xiao, S., & Weng, S. (2024). CTNet: a convolutional transformer network for EEG-based motor imagery classification. Scientific Reports, 14(1), 20237.
+2. Zhao, W., Jiang, X., Zhang, B., Xiao, S., & Weng, S. (2024). CTNet source code: https://github.com/snailpt/CTNet
 
- **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 = CTNet.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,