check the attached file

Dear Krah Kouadio ,

The Multi-Label Image Classification focuses on predicting labels for images in a multi-class classification problem where each image may belong to more than one class.

https://paperswithcode.com/task/multi-label-image-classification

Regards,

Shafagat

Dear Krah Kouadio ,

The following is a complete MATLAB code for multi-label image classification with comments to explain each step. This code uses a pre-trained ResNet-50 model for multi-label classification. Please note that you will need to adapt this code to your specific dataset, and you may need to fine-tune the hyperparameters and model architecture as needed.

----------------------------------------------------------------------------

% Multi-label Image Classification using a Pre-trained Model

% Step 1: Load and Preprocess the Data

% Ensure your dataset is organized with image files and corresponding labels.

% Define the folder containing your dataset

dataFolder = 'path_to_dataset_folder';

% Create an ImageDatastore for your dataset

imds = imageDatastore(dataFolder, 'IncludeSubfolders', true, 'LabelSource', 'foldernames');

% Specify data augmentation and preprocessing options if needed

% For example, to resize images to a fixed size:

% augmenter = imageDataAugmenter('RandXReflection', true, 'RandXTranslation', [-10 10], 'RandYTranslation', [-10 10]);

% imds.ReadFcn = @(filename)augmentedImageDatastore([224 224],filename,augmenter);

% Step 2: Load a Pre-trained Deep Learning Model

net = resnet50; % Load a pre-trained ResNet-50 model

% Step 3: Modify the Network for Multi-Label Classification

numClasses = 7; % Number of classes

layers = [

net.Layers(1:end-3)

fullyConnectedLayer(numClasses, 'Name', 'fc')

sigmoidLayer('Name', 'sigmoid')

];

% Step 4: Specify Training Options

options = trainingOptions('adam', ...

'MaxEpochs', 10, ...

'MiniBatchSize', 32, ...

'Shuffle', 'every-epoch', ...

'Plots', 'training-progress', ...

'ExecutionEnvironment', 'auto');

% Step 5: Train the Model

% Make sure to provide the correct trainingData and validationData

% These datasets should be properly preprocessed and formatted for training

net = trainNetwork(trainingData, layers, options);

% Step 6: Make Predictions

% testData should contain the images you want to classify

predictions = classify(net, testData);

% Step 7: Evaluate Model Performance

% Evaluate the model's performance using appropriate metrics

% Adjust trueLabels to match your dataset's ground truth labels

[precision, recall, f1score] = multilabelMetrics(trueLabels, predictions);

disp(['Precision: ' num2str(precision)]);

disp(['Recall: ' num2str(recall)]);

disp(['F1 Score: ' num2str(f1score)]);

% Step 8: Fine-Tune and Optimize

% Based on evaluation results, fine-tune the model, adjust hyperparameters, and apply data preprocessing techniques as needed.

----------------------------------------------------------------------------

Please replace 'path_to_dataset_folder', 'trainingData', 'validationData', 'testData', and 'trueLabels' with your actual data and labels. Additionally, adjust the preprocessing steps and data augmentation according to your specific dataset and requirements.

Krah Kouadio I have code in Python. If you are interested, let me know.

Check this sample,

Python

from keras.models import Sequential from keras.layers import Dense, Dropout, Activation from keras.optimizers import SGD from keras.preprocessing.image import ImageDataGenerator # Define the model architecture model = Sequential() model.add(Dense(64, input_dim=1000)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(64)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(10)) model.add(Activation('softmax')) # Compile the model sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) # Train the model train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) test_datagen = ImageDataGenerator(rescale=1./255) train_generator = train_datagen.flow_from_directory('train', target_size=(150, 150), batch_size=32, class_mode='binary') validation_generator = test_datagen.flow_from_directory('validation', target_size=(150, 150), batch_size=32, class_mode='binary') model.fit_generator(train_generator, samples_per_epoch=2000, nb_epoch=50, validation_data=validation_generator, nb_val_samples=800) # Evaluate the model score = model.evaluate_generator(validation_generator, nb_val_samples=800) print('Test score:', score[0]) print('Test accuracy:', score[1])