muawiaimti/efficientnet0

EfficientNetB0 Eye Disease Classification Model Model Overview This is a pre-trained EfficientNetB0 model for eye disease classification, saved in HDF5 format (efficientnet0.h5). It classifies eye images into one of the following categories:

Classes:

Normal Diabetic Retinopathy Glaucoma Myopia AMD (Age-related Macular Degeneration) Hypertension Not an eye Image Others

Architecture: EfficientNetB0

Framework: TensorFlow/Keras

Task: Image Classification

Input: Images resized to 224x224 pixels, normalized to [0, 1]

Output: Predicted disease label (one of the above classes)

Usage To use this model for inference, you can load it using TensorFlow/Keras:

Install dependencies: pip install tensorflow pillow numpy

Load the model in Python: from tensorflow.keras.models import load_model model = load_model("efficientnet0.h5")

Preprocess your image (resize to 224x224, convert to RGB if needed, normalize), then run inference: from PIL import Image import numpy as np img = Image.open("your_image.jpg") img_d = img.resize((224, 224)) if len(np.array(img_d).shape) < 3: rgb_img = Image.new("RGB", img_d.size) rgb_img.paste(img_d) else: rgb_img = img_d rgb_img = np.array(rgb_img, dtype=np.float64) rgb_img = rgb_img.reshape(1, 224, 224, 3) predictions = model.predict(rgb_img) predicted_class = int(np.argmax(predictions)) classes = ["Normal", "Diabetic Retinopathy", "Glaucoma", "Myopia", "AMD", "Hypertension", "Not an eye Image", "Others"] predicted_disease = classes[predicted_class] if 0 <= predicted_class < len(classes) else "Unknown"

Notes

This model is hosted on Hugging Face for storage. The Inference API may not support .h5 models directly. For inference, you can download the model and run it locally, or deploy a custom backend (e.g., using Flask). The model expects images to be preprocessed as described above.

License Specify your license here (e.g., MIT, Apache 2.0).