Welcome to the TensorFlow Image Recognition Tutorial! This guide will walk you through building a basic image classification model using TensorFlow.

🧠 Step-by-Step Guide

  1. Install TensorFlow
    Start by installing TensorFlow via pip: 

    pip install tensorflow

    📌 Learn more about TensorFlow installation

  2. Prepare Dataset
    Use the CIFAR-10 dataset for training: 

    from tensorflow.keras.datasets import cifar10  
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

    📷 Sample CIFAR-10 images

  3. Build the Model
    Create a convolutional neural network (CNN) architecture: 

    model = tf.keras.models.Sequential([  
    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)),  
    tf.keras.layers.MaxPooling2D(2,2),  
    tf.keras.layers.Flatten(),  
    tf.keras.layers.Dense(128, activation='relu'),  
    tf.keras.layers.Dense(10, activation='softmax')  
])

    📌 Explore CNN architecture details

  4. Train the Model
    Compile and train the model: 

    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])  
model.fit(x_train, y_train, epochs=10)

    📈 View training loss curve

  5. Evaluate & Deploy
    Test the model and deploy it for inference: 

    test_loss = model.evaluate(x_test, y_test)  
predictions = model.predict(x_test)

    🧩 Check model performance metrics

📚 Further Learning

TensorFlow Logo
Image Classification Workflow