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oliversinn/
waferguard-cnn

Name: waferguard-cnn
Availability: InStock

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waferguard-cnn

Description

Custom CNN for wafer defect classification

Model Details

Framework: Keras (TensorFlow backend)
Input: (52, 52, 3) normalized wafer map images
Output: 38-class probability distribution
Task: Multi-class wafer defect pattern classification

Metrics

Macro F1: 0.9736

Usage

python

from tensorflow import keras
from huggingface_hub import hf_hub_download
import numpy as np

# Download model
model_path = hf_hub_download("username/waferguard-cnn", filename="model.keras")
model = keras.models.load_model(model_path)

# Predict on wafer map (52, 52, 3)
wafer_map = np.random.randint(0, 3, (1, 52, 52, 3)).astype(np.float32)
predictions = model.predict(wafer_map)
predicted_class = np.argmax(predictions[0])
confidence = predictions[0, predicted_class]

print(f"Class: {predicted_class}, Confidence: {confidence:.2%}")

Dataset

Trained on WaferMap dataset from Kaggle:

https://www.kaggle.com/datasets/qingyi/wafer-map-defect

License

MIT License

oliversinn/waferguard-cnn