Image Classification Project Code in Machine Learning 📸🧠

Welcome to the Image Classification Project Code guide! This tutorial will walk you through building a basic image classification model using TensorFlow/Keras. Let's dive in!

Project Structure 📁

image-classification/
│
├── data/              # Preprocessed dataset
├── models/            # Model architecture files
├── train.py            # Training script
├── evaluate.py         # Evaluation script
└── requirements.txt    # Python dependencies

Key Steps 🧰

• Data Preparation 📚
  Use Dataset_Preprocessing to split data into training/validation sets.

  

• Model Building 🏗️
  Implement a CNN architecture:

  model = Sequential([
      Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),
      MaxPooling2D(2,2),
      Conv2D(64, (3,3), activation='relu'),
      MaxPooling2D(2,2),
      Flatten(),
      Dense(512, activation='relu'),
      Dense(10, activation='softmax')
  ])
  

  

• Training Process 🔄
  Compile and train the model:

  model.compile(loss='categorical_crossentropy',
                optimizer='adam',
                metrics=['accuracy'])
  history = model.fit(train_generator, epochs=10, validation_data=val_generator)
  

  

• Evaluation Metrics 📊
  Analyze performance with:

  • Accuracy
  • Confusion Matrix
  • F1 Score
  

Expand Your Knowledge 🌐

For deeper insights into image classification techniques, check out our tutorial on CNN architectures.