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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- clothes |
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- fashion |
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- machine learning |
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--- |
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ParticleNet: AI for Identifying Particles on Clothes |
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Overview |
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ParticleNet is a deep learning model designed to identify various types of particles on clothes. Utilizing convolutional neural networks (CNNs) for feature extraction, it processes images to classify different particle types with high accuracy. This model can be applied in quality control for the textile industry, ensuring garments are free from unwanted particles. |
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Model Architecture |
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ParticleNet features a series of convolutional layers with batch normalization and max pooling, followed by fully connected layers and a dropout layer to prevent overfitting. The final layer uses a softmax activation to output class probabilities. |
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Training |
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The model is trained using labeled images of clothes with different particles. Data augmentation and normalization techniques are employed to enhance generalization. |
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from tensorflow.keras.preprocessing.image import ImageDataGenerator |
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# Assuming you have training and validation directories with subdirectories for each class |
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train_datagen = ImageDataGenerator(rescale=1./255) |
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val_datagen = ImageDataGenerator(rescale=1./255) |
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train_generator = train_datagen.flow_from_directory( |
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'path_to_train_data', |
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target_size=(128, 128), |
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batch_size=32, |
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class_mode='categorical' |
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) |
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validation_generator = val_datagen.flow_from_directory( |
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'path_to_validation_data', |
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target_size=(128, 128), |
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batch_size=32, |
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class_mode='categorical' |
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) |
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history = model.fit( |
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train_generator, |
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epochs=10, # Adjust based on your needs |
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validation_data=validation_generator |
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) |
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Performance |
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ParticleNet achieves excellent performance on a variety of particle types, making it a reliable tool for automated inspection systems. |
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import tensorflow as tf |
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from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization |
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from tensorflow.keras.models import Model |
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def build_particle_net(input_shape, num_classes): |
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inputs = Input(shape=input_shape) |
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x = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs) |
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x = MaxPooling2D((2, 2))(x) |
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x = BatchNormalization()(x) |
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x = Conv2D(64, (3, 3), activation='relu', padding='same')(x) |
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x = MaxPooling2D((2, 2))(x) |
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x = BatchNormalization()(x) |
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x = Conv2D(128, (3, 3), activation='relu', padding='same')(x) |
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x = MaxPooling2D((2, 2))(x) |
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x = BatchNormalization()(x) |
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x = Flatten()(x) |
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x = Dense(128, activation='relu')(x) |
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x = Dropout(0.5)(x) |
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x = Dense(num_classes, activation='softmax')(x) |
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model = Model(inputs=inputs, outputs=x) |
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return model |
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# Example usage: |
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input_shape = (128, 128, 3) |
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num_classes = 10 |
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particle_net = build_particle_net(input_shape, num_classes) |
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particle_net.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) |
