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()

이미지 분류기를 통해 시각적 특징과 패턴을 분석하여 정확하게 이미지를 분류해보세요. 이미지 인식, 콘텐츠 필터링, 시각적 검색 등 다양한 분야에서 활용됩니다!

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