Update README.md

README.md

@@ -1,33 +1,76 @@
 ---
 tags:
 - image-classification
-- keras
+- tensorflow
+- keras-tuner
+- computer-vision
 datasets:
 - Iris314/Food_tomatoes_dataset
-license: mit
 ---
 
-# Best Tomato Classification Model
+# Tomato Binary Classification Model
 
-This repository contains a convolutional neural network model trained for binary classification of tomatoes using the [Iris314/Food_tomatoes_dataset](https://huggingface.co/datasets/Iris314/Food_tomatoes_dataset).
+This model is a convolutional neural network trained to classify images of tomatoes into two categories (presumably ripe and unripe, based on the dataset name and binary classification setup).
 
-## Dataset
+## Model Architecture
 
-The model was trained on the "augmented" split and validated on the "original" split of the `Iris314/Food_tomatoes_dataset`. This dataset contains images of tomatoes and non-tomatoes.
+The model architecture was determined using Keras Tuner's Hyperband algorithm. Based on the previous tuning results, the best hyperparameters found were:
+- `conv_blocks`: 2
+- `filters_0`: 32
+- `dense_units`: 64
+- `dropout`: 0.1
+- `lr`: 0.001
+- `filters_1`: 16
 
-## Model Training
+The model consists of:
+- Data augmentation layers (RandomFlip, RandomRotation, RandomZoom) applied during training.
+- Two convolutional blocks:
+    - The first block has 32 filters, a 3x3 kernel, ReLU activation, and MaxPooling.
+    - The second block has 16 filters, a 3x3 kernel, ReLU activation, and MaxPooling.
+- A Flatten layer.
+- A dense layer with 64 units and ReLU activation.
+- A Dropout layer with a rate of 0.1.
+- An output layer with a single unit and a sigmoid activation function for binary classification.
 
-The model is a Keras convolutional neural network whose architecture and hyperparameters were optimized using Keras Tuner, an AutoML library. The search was conducted with a fixed budget and early stopping based on validation loss.
+## Training
+
+- **Dataset:** Iris314/Food_tomatoes_dataset. The `augmented` split was used for training, and the `original` split was used for validation.
+- **Input Resolution:** Images are resized to 128x128 pixels.
+- **Preprocessing:** Images are converted to RGB and pixel values are scaled to the range [0, 1].
+- **Optimizer:** Adam with a learning rate of 0.001 (based on the best hyperparameters).
+- **Loss Function:** Binary Crossentropy.
+- **Metrics:** Accuracy was used as the evaluation metric.
+- **Early Stopping:** Training was stopped early if the validation loss did not improve for 3 consecutive epochs. The model was trained for a maximum of 15 epochs.
+
+## Performance
+
+Based on the evaluation on the validation set, the model achieved the following performance:
+
+- **Accuracy:** 1.00
+- **Loss:** 0.0079
+
+**Classification Report:**
 
-The best hyperparameters found by Keras Tuner were:
 ```
 import tensorflow as tf
-# Load the model
-model = tf.keras.models.load_model("best_tomato_model.keras")
-
-Example prediction (replace with your image loading and preprocessing)
-image = ... # Load and preprocess your image
-image = tf.expand_dims(image, axis=0) # Add batch dimension
-prediction = model.predict(image)
-print(prediction)
+from PIL import Image
+import numpy as np
+
+#Load the model
+model = tf.keras.models.load_model('best_tomato_model.keras')
+
+#Load and preprocess an image
+img_path = 'path/to/your/image.jpg' # Replace with your image path
+img = Image.open(img_path).convert('RGB').resize((128, 128))
+img_array = np.array(img) / 255.0
+img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
+
+#Make a prediction
+prediction = model.predict(img_array)
+
+#Interpret the prediction
+predicted_class = int(prediction > 0.5)
+
+print(f"Prediction: {prediction[0][0]:.4f}")
+print(f"Predicted class: {predicted_class}")
 ```