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README.md

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+# Corn Detection Model
+
+This repository contains an implementation of a corn detection model using the EfficientNet architecture. The model distinguishes between "Healthy corn" and "Infected" corn based on input images.
+
 ---
-license: mit
-language:
-- en
-metrics:
-- accuracy
-pipeline_tag: image-classification
----
+
+## Overview
+
+The project uses **EfficientNetB3** as the base model and is fine-tuned for corn health detection. It supports image classification by preprocessing input images to the required dimensions and scale, and then outputs predictions with associated confidence scores.
+
+---
+
+## Model Details
+
+- **Model Type:** EfficientNet
+- **Base Model:** EfficientNetB3
+- **Weights File:** `EfficientNetB3-corn-100.0.h5`
+- **License:** MIT
+- **Language:** English
+- **Main Metric:** Accuracy
+- **Pipeline Tag:** Image Classification
+
+### Classes
+
+1. **Healthy corn**
+   - **ID:** 0
+   - **Input Size:** 224 x 224 pixels
+   - **Scale Factor:** 1
+2. **Infected**
+   - **ID:** 1
+   - **Input Size:** 224 x 224 pixels
+   - **Scale Factor:** 1
+
+### Preprocessing
+
+- **Resize:** `[224, 224]`
+- **Scale:** Images are scaled by `255` (i.e., pixel values are normalized)
+
+---
+
+## Installation
+
+Ensure you have Python installed along with the necessary dependencies. You can install the required packages with pip:
+
+```bash
+pip install tensorflow huggingface_hub numpy pillow requests
+```
+
+---
+
+## Usage
+
+### Custom Depthwise Convolution Layer
+
+Due to a potential mismatch with the default Keras implementation, a custom wrapper for the `DepthwiseConv2D` layer is provided that ignores the `groups` parameter. This wrapper is then used when loading the model.
+
+### Loading the Model
+
+The model is downloaded from the Hugging Face Hub using the `hf_hub_download` function and loaded with the custom `DepthwiseConv2D` object:
+
+```python
+from tensorflow.keras.layers import DepthwiseConv2D as OriginalDepthwiseConv2D
+from huggingface_hub import hf_hub_download
+from tensorflow.keras.models import load_model
+
+# Define a wrapper that ignores the 'groups' argument
+def DepthwiseConv2D(*args, **kwargs):
+    kwargs.pop('groups', None)  # Remove the groups parameter if present
+    return OriginalDepthwiseConv2D(*args, **kwargs)
+
+# Download the model weights from the Hugging Face Hub
+model_path = hf_hub_download(
+    repo_id="Luwayy/corn-detection",  # Your HF repository ID
+    filename="EfficientNetB3-corn-100.0.h5"
+)
+
+custom_objects = {'DepthwiseConv2D': DepthwiseConv2D}
+model = load_model(model_path, custom_objects=custom_objects)
+```
+
+### Preprocessing and Prediction
+
+The code below demonstrates how to load and preprocess an image, and then perform prediction:
+
+```python
+import numpy as np
+from tensorflow.keras.applications.efficientnet import preprocess_input
+from PIL import Image
+import requests
+from io import BytesIO
+
+# Class labels
+labels = ["Healthy corn", "Infected"]
+
+# Function to load and preprocess the image
+def load_and_preprocess_image(image_url):
+    response = requests.get(image_url)
+    img = Image.open(BytesIO(response.content)).convert("RGB")
+    img = img.resize((224, 224))  # Resize to model input dimensions
+    img_array = np.array(img)
+    img_array = preprocess_input(img_array)  # EfficientNet preprocessing
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    return img_array
+
+# Prediction function
+def predict(image_url):
+    img = load_and_preprocess_image(image_url)
+    preds = model.predict(img)[0]
+    pred_index = np.argmax(preds)
+    confidence = preds[pred_index]
+    return labels[pred_index], confidence
+
+# Example usage
+image_url = "https://www.harvestplus.org/wp-content/uploads/2021/08/Orange-maize-2.png"  # Replace with your image URL
+predicted_class, confidence = predict(image_url)
+print(f"Predicted: {predicted_class} (Confidence: {confidence:.2f})")
+```
+
+Upon running the example, you might see an output similar to:
+
+```
+Predicted: Healthy corn (Confidence: 0.80)
+```
+
+---
+