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TPnet-small
Yukin3/TPnet-small
TPnet-small is a tabular classification model from Yukin3. Use it for the tabular classification task on the model card, and read the license before you ship it in a product. It is set up for keras. The card lists the license as mit.
TPnet-small is a lightweight deep neural network (DNN) designed to predict traffic congestion using tabular smart mobility features. It serves as a compact yet powerful alternative to tree-based models.
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Public
Updated Apr 30, 2025
Repo size
196 KB
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At a glance
- Task
- Tabular Classification
- Library
- keras
- License
- mit
- Access
- Public
- Created
- Apr 30, 2025
- Updated
- Apr 30, 2025
- SHA
- 911883a4
Model card
From the Hugging Face model README
TPnet-small
TPnet-small is a lightweight deep neural network (DNN) designed to predict traffic congestion using tabular smart mobility features. It serves as a compact yet powerful alternative to tree-based models.
Model Details
- Model type: Feedforward Deep Neural Network (3 hidden layers)
- Architecture: [128, 64, 32] with ReLU activations and Dropout
- Output: 3-class Softmax output (
High,Medium,Low) - Trained on: Smart Mobility Traffic Dataset from Kaggle
Training Details
- Optimizer: Adam
- Loss: Sparse Categorical Crossentropy
- Epochs: 50
- Batch size: 32
- Training time: ~6 seconds on CPU
- Train Accuracy: 99.1%
- Val Accuracy: 94.6%
Evaluation
| Metric | Value |
|---|---|
| Accuracy | 94.5% |
| F1 Score | 0.944 |
| Parameters | 13,123 |
| Model Size | ~156 KB (.h5 format) |
Includes training trajectory and confusion matrix plots.
How to Use
from tensorflow.keras.models import load_model
model = load_model("traffic_predictor_dnn.h5")
y_pred = model.predict(X_test) # X_test must be scaled [n_samples, 20]
Limitations
-
Performance limited by small dataset size and feature coverage
-
Currently optimized for CPU inference, not edge deployment
Authors
- Created by @Yukin3
- Task
- Tabular Classification
- Library
- keras
- License
- mit
- Languages
- en
- Created
- Apr 30, 2025
- Updated
- Apr 30, 2025