Update README.md

README.md

@@ -1,29 +1,67 @@
----
-library_name: pytorch
-tags:
-- tabular
-- pytorch
-- classification
-- aviation
-- wing-design
-datasets:
-- ecopus/transport-wings-500
-license: mit
----
-
-# Wing Selector MLP
-
-This repository contains a PyTorch MLP that scores aircraft-style wings **within the same airfoil** for a chosen objective:
-- **min_cd** (minimize drag),
-- **max_cl** (maximize lift),
-- **max_ld** (maximize lift-to-drag).
-
-It was trained on the dataset **[ecopus/transport-wings-500](https://huggingface.co/datasets/ecopus/transport-wings-500)**.
-
-## Files
-- `best.pt` – best checkpoint by validation top-1@group  
-- `last.pt` – final checkpoint after training  
-- `config.json` – input dim, #airfoils, feature scaler stats  
-- `feature_names.json` – expected feature order  
-- `airfoil_vocab.json` – airfoil name → id mapping used during training  
-- `inference.py` – minimal loader & scoring helper
+---
+library_name: pytorch
+tags:
+- tabular
+- pytorch
+- classification
+- aviation
+- wing-design
+datasets:
+- ecopus/transport-wings-500
+license: mit
+---
+
+# Wing Selector MLP
+
+This repository contains a PyTorch MLP that scores aircraft-style wings **within the same airfoil** for a chosen objective:
+- **min_cd** (minimize drag),
+- **max_cl** (maximize lift),
+- **max_ld** (maximize lift-to-drag).
+
+It was trained on the dataset **[ecopus/transport-wings-500](https://huggingface.co/datasets/ecopus/transport-wings-500)**.
+
+## Files
+- `best.pt` – best checkpoint by validation top-1@group  
+- `last.pt` – final checkpoint after training  
+- `config.json` – input dim, #airfoils, feature scaler stats  
+- `feature_names.json` – expected feature order  
+- `airfoil_vocab.json` – airfoil name → id mapping used during training  
+- `inference.py` – minimal loader & scoring helper
+
+---
+
+### Model Architecture
+
+The model is a feedforward neural network designed for a binary classification task. It predicts the "best" wing geometry for a given airfoil and aerodynamic objective.
+
+* **Inputs**: The model takes three inputs:
+    1.  **Wing Features**: A vector of 22 continuous features describing the wing's geometry and aerodynamic properties. These features are standardized (mean-centered and scaled by standard deviation) before being fed into the model.
+    2.  **Objective ID**: A one-hot encoded vector representing one of the three possible design objectives.
+    3.  **Airfoil ID**: An embedding vector that learns a representation for each unique airfoil in the training data.
+* **Embedding Layer**: An `nn.Embedding` layer converts the discrete airfoil ID into a dense 8-dimensional vector.
+* **Hidden Layers**: The core of the network consists of two fully connected hidden layers, each with **128 neurons** and using the **ReLU** activation function.
+* **Output Layer**: A final linear layer outputs a single logit, which represents the model's prediction score.
+
+---
+
+### Training Hyperparameters
+
+| Hyperparameter | Value |
+| :--- | :--- |
+| **Epochs** | 50 |
+| **Batch Size** | 64 |
+| **Learning Rate** | `2e-3` |
+| **Optimizer** | AdamW |
+| **LR Scheduler** | CosineAnnealingLR |
+| **Loss Function** | BCEWithLogitsLoss |
+| **Seed** | 42 |
+
+---
+
+### Final Training Metrics
+
+| Metric | Value |
+| :--- | :--- |
+| **Validation AUC** | 0.9790 |
+| **Validation Avg. Precision**| 0.638 |
+| **Validation Top-1 Accuracy**| 50.0% |