import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
image_directory = "/path/to/image/directory"
image_size = (256, 256)
batch_size = 32
image_generator = ImageDataGenerator(rescale=1./255, validation_split=0.2)
train_data = image_generator.flow_from_directory(
image_directory,
target_size=image_size,
batch_size=batch_size,
class_mode='binary',
subset='training')
validation_data = image_generator.flow_from_directory(
image_directory,
target_size=image_size,
batch_size=batch_size,
class_mode='binary',
subset='validation')
model= Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D((2, 2)),
Conv2D(128, (3, 3), activation='relu'),
MaxPooling2D((2, 2)),
Flatten(),
Dense(128, activation='relu'),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
history = model.fit(train_data, epochs=10, validation_data=validation_data)
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.plot(history.history['loss'], label='loss')
plt.plot(history.history['val_loss'], label='val_loss')
plt.legend()
plt.show()
Image Classifier
Revolutionize fruit classification with our advanced Fruit Classifier, harnessing the power of machine learning and computer vision for accurate fruit image classification. Transforming agriculture, inventory management, and visual search like never before.
