Image Classifier WebUI

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()

利用機器學習的圖像分類系統，分析視覺特徵和模式，精準分類不同類別的圖像，廣泛應用於圖像識別、內容過濾和視覺搜索！

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