MIT 6.S191: Convolutional Neural Networks - Deep Computer Vision

Teaching Machines to See: From Pixels to Understanding

Introduction: The Vision Revolution

Computer vision represents one of the most transformative applications of deep learning, enabling machines to interpret and understand the visual world. Beyond simple recognition, modern computer vision systems can understand context, relationships, dynamics, and even predict future events from visual input.

Consider a street scene: humans effortlessly distinguish between parked and moving vehicles, infer pedestrian intent, understand traffic light states, and navigate complex spatial relationships. This remarkable capability emerges from our brain’s hierarchical visual processing system - an architecture that inspired convolutional neural networks.

Impact Across Industries

Autonomous Systems: Self-driving cars, drones, and robotic navigation Healthcare: Medical image analysis, diagnostic assistance, surgical robotics
Mobile Computing: Photo organization, augmented reality, visual search Security: Facial recognition, surveillance, anomaly detection Manufacturing: Quality control, defect detection, assembly automation Accessibility: Visual description for visually impaired, sign language recognition

Images as Data: The Numerical Foundation

From Pixels to Patterns

Computers process numerical data, and images naturally fit this paradigm as structured grids of numbers representing visual information.

Grayscale Images: 2D matrices (Height × Width)

Each pixel contains a single intensity value (typically 0-255)
0 represents black, 255 represents white
Intermediate values represent gray levels

Color Images: 3D tensors (Height × Width × 3 Channels)

Three color channels: Red, Green, Blue (RGB)
Each pixel represented by three intensity values
Enables full-color representation through additive color mixing

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
 
# Load and visualize image data
def load_and_process_image(image_path):
    # Load image
    image = tf.io.read_file(image_path)
    image = tf.image.decode_image(image, channels=3)
    image = tf.cast(image, tf.float32) / 255.0  # Normalize to [0,1]
    
    return image
 
# Example: CIFAR-10 dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
 
# Data shape: (50000, 32, 32, 3) - 50k images, 32x32 pixels, 3 color channels
print(f"Training data shape: {x_train.shape}")
print(f"Pixel value range: [{x_train.min()}, {x_train.max()}]")
 
# Visualize sample images
plt.figure(figsize=(10, 10))
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 
               'dog', 'frog', 'horse', 'ship', 'truck']
 
for i in range(25):
    plt.subplot(5, 5, i + 1)
    plt.imshow(x_train[i])
    plt.title(class_names[y_train[i][0]])
    plt.axis('off')
plt.show()

Computer Vision Tasks: A Taxonomy

Fundamental Task Categories

Image Classification: Assigning entire images to discrete categories

Single-label: One class per image
Multi-label: Multiple classes per image
Fine-grained: Distinguishing between similar categories

Object Detection: Locating and classifying objects within images

Bounding box prediction
Multiple objects per image
Real-time detection requirements

Semantic Segmentation: Pixel-level classification

Every pixel assigned a class label
Scene understanding applications
Medical image segmentation

Instance Segmentation: Combining detection and segmentation

Individual object instances
Precise boundary delineation
Robotics and manipulation applications

Regression Tasks: Predicting continuous values

Pose estimation
Depth prediction
Age estimation from facial images

The Feature Extraction Challenge

Traditional Computer Vision Limitations

Before deep learning, computer vision relied on manually crafted features designed by domain experts. This approach faced fundamental limitations:

Hand-Crafted Features (SIFT, HOG, Haar Cascades):

Required extensive domain knowledge
Brittle to variations in viewpoint, lighting, scale
Limited generalization capability
Time-intensive development process

Inherent Visual Challenges:

Viewpoint Variation: Objects appear different from different angles
Scale Changes: Objects vary in size within images
Illumination: Lighting conditions affect appearance dramatically
Deformation: Non-rigid objects change shape
Occlusion: Objects partially hidden by others
Background Clutter: Distracting background elements
Intra-class Variation: Large differences within object categories

The Deep Learning Solution

Automatic Feature Learning: Instead of hand-crafting features, let the network learn optimal representations directly from data.

Hierarchical Feature Extraction: Build complex features from simpler ones through multiple processing layers.

Translation Invariance: Features that remain consistent across spatial locations.

Robustness: Learned features adapt to handle various types of visual variation.

Convolutional Neural Networks: Architecture and Operations

The Convolution Operation

Convolution forms the foundation of CNNs, detecting local patterns through learned filters (kernels).

Mathematical Definition:

(I * K)[i,j] = ΣΣ I[i+m, j+n] × K[m,n]

Where:

I: Input image
K: Convolution kernel/filter
*: Convolution operation

Key Properties:

Local Connectivity: Each output connects to small input region
Parameter Sharing: Same filter applied across entire image
Translation Equivariance: Shifted input produces shifted output

Filter Examples and Feature Detection

import tensorflow as tf
import numpy as np
 
# Edge detection filters
horizontal_edge_filter = np.array([
    [-1, -1, -1],
    [ 0,  0,  0],
    [ 1,  1,  1]
], dtype=np.float32).reshape(3, 3, 1, 1)
 
vertical_edge_filter = np.array([
    [-1, 0, 1],
    [-1, 0, 1],
    [-1, 0, 1]
], dtype=np.float32).reshape(3, 3, 1, 1)
 
# Apply convolution manually
def apply_filter(image, filter_kernel):
    # Add batch and channel dimensions if needed
    if len(image.shape) == 2:
        image = image[None, :, :, None]
    
    # Perform convolution
    filtered = tf.nn.conv2d(
        image, 
        filter_kernel, 
        strides=[1, 1, 1, 1], 
        padding='SAME'
    )
    return filtered[0, :, :, 0]  # Remove batch and channel dimensions
 
# Example usage
sample_image = tf.random.normal((28, 28))
horizontal_edges = apply_filter(sample_image, horizontal_edge_filter)
vertical_edges = apply_filter(sample_image, vertical_edge_filter)

Convolutional Layer Implementation

# Basic convolutional layer
conv_layer = tf.keras.layers.Conv2D(
    filters=32,          # Number of filters
    kernel_size=3,       # Filter size (3x3)
    strides=1,           # Step size
    padding='same',      # Padding strategy
    activation='relu',   # Activation function
    input_shape=(28, 28, 1)
)
 
# Advanced convolutional layer with regularization
advanced_conv = tf.keras.layers.Conv2D(
    filters=64,
    kernel_size=(3, 3),
    strides=(1, 1),
    padding='same',
    activation='relu',
    kernel_regularizer=tf.keras.regularizers.l2(0.001),
    kernel_initializer='he_normal',
    use_bias=True
)

Pooling Operations: Spatial Dimension Reduction

Pooling reduces spatial dimensions while retaining important features:

Max Pooling: Selects maximum value in each region

Preserves strongest activations
Provides translation invariance
Reduces computational requirements

Average Pooling: Computes mean value in each region

Smoother down-sampling
Less aggressive feature selection
Better for some regression tasks

# Pooling layer examples
max_pool = tf.keras.layers.MaxPool2D(
    pool_size=2,    # 2x2 pooling window
    strides=2,      # Step size
    padding='valid' # No padding
)
 
avg_pool = tf.keras.layers.AveragePooling2D(
    pool_size=2,
    strides=2
)
 
# Global pooling for final feature extraction
global_avg_pool = tf.keras.layers.GlobalAveragePooling2D()
global_max_pool = tf.keras.layers.GlobalMaxPooling2D()

Complete CNN Architecture Design

Classical CNN Structure

The standard CNN follows a hierarchical pattern:

Feature Extraction: Alternating convolution and pooling layers
Feature Learning: Multiple convolutional blocks with increasing depth
Classification: Fully connected layers for final prediction

def build_cnn_classifier(input_shape, num_classes):
    model = tf.keras.Sequential([
        # First convolutional block
        tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=input_shape),
        tf.keras.layers.MaxPooling2D(2),
        
        # Second convolutional block
        tf.keras.layers.Conv2D(64, 3, activation='relu'),
        tf.keras.layers.MaxPooling2D(2),
        
        # Third convolutional block
        tf.keras.layers.Conv2D(128, 3, activation='relu'),
        tf.keras.layers.MaxPooling2D(2),
        
        # Fourth convolutional block
        tf.keras.layers.Conv2D(256, 3, activation='relu'),
        tf.keras.layers.MaxPooling2D(2),
        
        # Flatten for fully connected layers
        tf.keras.layers.Flatten(),
        
        # Dense layers for classification
        tf.keras.layers.Dense(512, activation='relu'),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(num_classes, activation='softmax')
    ])
    
    return model
 
# Build model for CIFAR-10
model = build_cnn_classifier((32, 32, 3), 10)
model.summary()

Modern CNN Best Practices

Batch Normalization: Stabilizes training and enables deeper networks

tf.keras.layers.Conv2D(64, 3, use_bias=False),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Activation('relu')

Residual Connections: Enable training of very deep networks

def residual_block(x, filters):
    shortcut = x
    
    x = tf.keras.layers.Conv2D(filters, 3, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)
    
    x = tf.keras.layers.Conv2D(filters, 3, padding='same')(x)
    x = tf.keras.layers.BatchNormalization()(x)
    
    # Add shortcut connection
    x = tf.keras.layers.Add()([x, shortcut])
    x = tf.keras.layers.Activation('relu')(x)
    
    return x

Depthwise Separable Convolutions: Efficient convolution operations

tf.keras.layers.SeparableConv2D(
    filters=128,
    kernel_size=3,
    padding='same',
    activation='relu'
)

Advanced CNN Architectures

LeNet-5 (1998): The Pioneer

def lenet5():
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(6, 5, activation='tanh', input_shape=(32, 32, 1)),
        tf.keras.layers.AveragePooling2D(2),
        tf.keras.layers.Conv2D(16, 5, activation='tanh'),
        tf.keras.layers.AveragePooling2D(2),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(120, activation='tanh'),
        tf.keras.layers.Dense(84, activation='tanh'),
        tf.keras.layers.Dense(10, activation='softmax')
    ])
    return model

AlexNet (2012): The Breakthrough

def alexnet():
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(96, 11, strides=4, activation='relu', input_shape=(227, 227, 3)),
        tf.keras.layers.MaxPooling2D(3, strides=2),
        tf.keras.layers.Conv2D(256, 5, padding='same', activation='relu'),
        tf.keras.layers.MaxPooling2D(3, strides=2),
        tf.keras.layers.Conv2D(384, 3, padding='same', activation='relu'),
        tf.keras.layers.Conv2D(384, 3, padding='same', activation='relu'),
        tf.keras.layers.Conv2D(256, 3, padding='same', activation='relu'),
        tf.keras.layers.MaxPooling2D(3, strides=2),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(4096, activation='relu'),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(4096, activation='relu'),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(1000, activation='softmax')
    ])
    return model

VGGNet: Deep and Simple

def vgg16():
    model = tf.keras.Sequential([
        # Block 1
        tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu', input_shape=(224, 224, 3)),
        tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),
        tf.keras.layers.MaxPooling2D(2, strides=2),
        
        # Block 2
        tf.keras.layers.Conv2D(128, 3, padding='same', activation='relu'),
        tf.keras.layers.Conv2D(128, 3, padding='same', activation='relu'),
        tf.keras.layers.MaxPooling2D(2, strides=2),
        
        # Block 3
        tf.keras.layers.Conv2D(256, 3, padding='same', activation='relu'),
        tf.keras.layers.Conv2D(256, 3, padding='same', activation='relu'),
        tf.keras.layers.Conv2D(256, 3, padding='same', activation='relu'),
        tf.keras.layers.MaxPooling2D(2, strides=2),
        
        # Block 4
        tf.keras.layers.Conv2D(512, 3, padding='same', activation='relu'),
        tf.keras.layers.Conv2D(512, 3, padding='same', activation='relu'),
        tf.keras.layers.Conv2D(512, 3, padding='same', activation='relu'),
        tf.keras.layers.MaxPooling2D(2, strides=2),
        
        # Block 5
        tf.keras.layers.Conv2D(512, 3, padding='same', activation='relu'),
        tf.keras.layers.Conv2D(512, 3, padding='same', activation='relu'),
        tf.keras.layers.Conv2D(512, 3, padding='same', activation='relu'),
        tf.keras.layers.MaxPooling2D(2, strides=2),
        
        # Classification head
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(4096, activation='relu'),
        tf.keras.layers.Dense(4096, activation='relu'),
        tf.keras.layers.Dense(1000, activation='softmax')
    ])
    return model

Training CNNs: Practical Considerations

Data Preprocessing and Augmentation

# Data preprocessing pipeline
def preprocess_data():
    datagen = tf.keras.preprocessing.image.ImageDataGenerator(
        rescale=1./255,              # Normalize pixel values
        rotation_range=20,           # Random rotation
        width_shift_range=0.2,       # Horizontal shift
        height_shift_range=0.2,      # Vertical shift
        horizontal_flip=True,        # Random horizontal flip
        zoom_range=0.2,              # Random zoom
        fill_mode='nearest'          # Fill strategy for new pixels
    )
    return datagen
 
# Modern data augmentation with tf.data
def augment_image(image, label):
    # Random flip
    image = tf.image.random_flip_left_right(image)
    
    # Random brightness
    image = tf.image.random_brightness(image, max_delta=0.1)
    
    # Random contrast
    image = tf.image.random_contrast(image, lower=0.9, upper=1.1)
    
    # Random saturation
    image = tf.image.random_saturation(image, lower=0.9, upper=1.1)
    
    return image, label
 
# Apply augmentation to dataset
train_dataset = train_dataset.map(
    augment_image,
    num_parallel_calls=tf.data.AUTOTUNE
)

Training Configuration

def train_cnn_model():
    # Model compilation
    model.compile(
        optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
        loss='sparse_categorical_crossentropy',
        metrics=['accuracy']
    )
    
    # Callbacks for training optimization
    callbacks = [
        tf.keras.callbacks.EarlyStopping(
            monitor='val_loss',
            patience=10,
            restore_best_weights=True
        ),
        tf.keras.callbacks.ReduceLROnPlateau(
            monitor='val_loss',
            factor=0.5,
            patience=5,
            min_lr=1e-7
        ),
        tf.keras.callbacks.ModelCheckpoint(
            'best_model.h5',
            monitor='val_accuracy',
            save_best_only=True
        )
    ]
    
    # Training
    history = model.fit(
        train_dataset,
        epochs=100,
        validation_data=val_dataset,
        callbacks=callbacks
    )
    
    return history

Transfer Learning: Leveraging Pre-trained Models

def build_transfer_learning_model(num_classes):
    # Load pre-trained base model
    base_model = tf.keras.applications.VGG16(
        weights='imagenet',
        include_top=False,
        input_shape=(224, 224, 3)
    )
    
    # Freeze base model layers
    base_model.trainable = False
    
    # Add custom classification head
    model = tf.keras.Sequential([
        base_model,
        tf.keras.layers.GlobalAveragePooling2D(),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(num_classes, activation='softmax')
    ])
    
    return model
 
# Fine-tuning strategy
def fine_tune_model(model, base_model):
    # Initial training with frozen base
    model.compile(
        optimizer=tf.keras.optimizers.Adam(1e-3),
        loss='sparse_categorical_crossentropy',
        metrics=['accuracy']
    )
    
    initial_history = model.fit(train_dataset, epochs=10, validation_data=val_dataset)
    
    # Unfreeze and fine-tune with lower learning rate
    base_model.trainable = True
    model.compile(
        optimizer=tf.keras.optimizers.Adam(1e-5),
        loss='sparse_categorical_crossentropy',
        metrics=['accuracy']
    )
    
    fine_tune_history = model.fit(
        train_dataset,
        epochs=10,
        initial_epoch=10,
        validation_data=val_dataset
    )
    
    return initial_history, fine_tune_history

Modern Computer Vision Applications

Object Detection: YOLO Architecture

# Simplified YOLO-style detection head
def yolo_detection_head(inputs, num_classes, num_anchors=3):
    # Prediction: [batch, grid_h, grid_w, anchors * (5 + num_classes)]
    # 5 = x, y, w, h, confidence
    predictions = tf.keras.layers.Conv2D(
        filters=num_anchors * (5 + num_classes),
        kernel_size=1,
        activation='linear'
    )(inputs)
    
    return predictions
 
# Complete detection model
def build_yolo_model(input_shape, num_classes):
    inputs = tf.keras.layers.Input(shape=input_shape)
    
    # Backbone (feature extractor)
    x = tf.keras.layers.Conv2D(32, 3, activation='relu', padding='same')(inputs)
    x = tf.keras.layers.MaxPooling2D(2)(x)
    
    x = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same')(x)
    x = tf.keras.layers.MaxPooling2D(2)(x)
    
    x = tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same')(x)
    x = tf.keras.layers.MaxPooling2D(2)(x)
    
    # Detection head
    outputs = yolo_detection_head(x, num_classes)
    
    model = tf.keras.Model(inputs, outputs)
    return model

Semantic Segmentation: U-Net Architecture

def unet_model(input_size, num_classes):
    inputs = tf.keras.Input(input_size)
    
    # Encoder (downsampling path)
    c1 = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same')(inputs)
    c1 = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same')(c1)
    p1 = tf.keras.layers.MaxPooling2D(2)(c1)
    
    c2 = tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same')(p1)
    c2 = tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same')(c2)
    p2 = tf.keras.layers.MaxPooling2D(2)(c2)
    
    c3 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='same')(p2)
    c3 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='same')(c3)
    p3 = tf.keras.layers.MaxPooling2D(2)(c3)
    
    # Bottleneck
    c4 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same')(p3)
    c4 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same')(c4)
    
    # Decoder (upsampling path)
    u3 = tf.keras.layers.UpSampling2D(2)(c4)
    u3 = tf.keras.layers.concatenate([u3, c3])
    c5 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='same')(u3)
    c5 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='same')(c5)
    
    u2 = tf.keras.layers.UpSampling2D(2)(c5)
    u2 = tf.keras.layers.concatenate([u2, c2])
    c6 = tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same')(u2)
    c6 = tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same')(c6)
    
    u1 = tf.keras.layers.UpSampling2D(2)(c6)
    u1 = tf.keras.layers.concatenate([u1, c1])
    c7 = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same')(u1)
    c7 = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same')(c7)
    
    # Output layer
    outputs = tf.keras.layers.Conv2D(num_classes, 1, activation='softmax')(c7)
    
    model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
    return model

Performance Evaluation and Analysis

Metrics for Computer Vision Tasks

# Classification metrics
def evaluate_classification(model, test_data):
    predictions = model.predict(test_data)
    predicted_classes = np.argmax(predictions, axis=1)
    
    # Accuracy
    accuracy = tf.keras.metrics.Accuracy()
    accuracy.update_state(y_test, predicted_classes)
    
    # Top-k accuracy
    top5_accuracy = tf.keras.metrics.TopKCategoricalAccuracy(k=5)
    top5_accuracy.update_state(y_test, predictions)
    
    return {
        'accuracy': accuracy.result().numpy(),
        'top5_accuracy': top5_accuracy.result().numpy()
    }
 
# Detection metrics (simplified IoU)
def calculate_iou(box1, box2):
    """Calculate Intersection over Union of two bounding boxes"""
    x1, y1, w1, h1 = box1
    x2, y2, w2, h2 = box2
    
    # Calculate intersection
    xi1 = max(x1, x2)
    yi1 = max(y1, y2)
    xi2 = min(x1 + w1, x2 + w2)
    yi2 = min(y1 + h1, y2 + h2)
    
    if xi2 <= xi1 or yi2 <= yi1:
        return 0
    
    intersection = (xi2 - xi1) * (yi2 - yi1)
    union = w1 * h1 + w2 * h2 - intersection
    
    return intersection / union

Visualization and Interpretation

def visualize_feature_maps(model, image):
    # Extract intermediate layer outputs
    layer_names = ['conv2d', 'conv2d_1', 'conv2d_2']  # First 3 conv layers
    intermediate_model = tf.keras.Model(
        inputs=model.input,
        outputs=[model.get_layer(name).output for name in layer_names]
    )
    
    # Get feature maps
    feature_maps = intermediate_model.predict(image[None, :, :, :])
    
    # Visualize
    fig, axes = plt.subplots(len(layer_names), 8, figsize=(20, 8))
    
    for layer_idx, fmap in enumerate(feature_maps):
        for i in range(8):  # Show first 8 filters
            ax = axes[layer_idx, i]
            ax.imshow(fmap[0, :, :, i], cmap='viridis')
            ax.set_title(f'{layer_names[layer_idx]}_filter_{i}')
            ax.axis('off')
    
    plt.tight_layout()
    plt.show()
 
def visualize_filters(model, layer_name):
    # Extract filter weights
    layer = model.get_layer(layer_name)
    filters, biases = layer.get_weights()
    
    # Normalize filters for visualization
    f_min, f_max = filters.min(), filters.max()
    filters = (filters - f_min) / (f_max - f_min)
    
    # Plot filters
    n_filters = filters.shape[3]
    ix = 1
    
    for i in range(n_filters):
        f = filters[:, :, :, i]
        
        for j in range(f.shape[2]):  # For each input channel
            plt.subplot(n_filters, f.shape[2], ix)
            plt.imshow(f[:, :, j], cmap='gray')
            ix += 1
    
    plt.show()

Key Insights and Best Practices

Architecture Design Principles

Hierarchical Feature Learning: Start with simple edges/textures, build to complex objects
Spatial Hierarchy: Gradually reduce spatial dimensions while increasing depth
Parameter Efficiency: Use convolution’s parameter sharing advantage
Regularization: Employ dropout, batch normalization, and data augmentation

Training Strategies

Transfer Learning: Leverage pre-trained models when possible
Progressive Training: Start simple, gradually increase complexity
Data Augmentation: Essential for robust performance
Learning Rate Scheduling: Adaptive learning rates improve convergence

Modern Trends and Future Directions

Vision Transformers (ViTs): Applying transformer architectures to computer vision Neural Architecture Search (NAS): Automated architecture optimization Efficient Architectures: MobileNets, EfficientNets for mobile deployment Self-Supervised Learning: Reducing reliance on labeled data

Summary and Next Steps

Convolutional Neural Networks have revolutionized computer vision by learning hierarchical feature representations directly from data. Key achievements include:

Automatic Feature Learning: Eliminates manual feature engineering
Translation Invariance: Robust to spatial variations
Hierarchical Representation: Builds complex understanding from simple patterns
Scalable Architecture: Handles variable input sizes and complexities

Continue Your Journey

Deep Generative Modeling: Creating new visual content
Deep Reinforcement Learning: Vision-guided decision making
NVIDIA CUDA: From History to AI Revolution: Optimizing CNN training performance and implementing efficient vision algorithms

Your Brain is a Self-Learning AI: Biological vision system inspiration
NVIDIA CUDA: From History to AI Revolution: Hardware enabling modern computer vision
Deep Learning Fundamentals: Foundation concepts

This article is part of the MIT 6.S191 Deep Learning Series. Explore hands-on computer vision labs at introtodeeplearning.com.

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MIT 6.S191: Convolutional Neural Networks - Deep Computer Vision

MIT 6.S191: Convolutional Neural Networks - Deep Computer Vision

Introduction: The Vision Revolution

Impact Across Industries

Images as Data: The Numerical Foundation

From Pixels to Patterns

Computer Vision Tasks: A Taxonomy

Fundamental Task Categories

The Feature Extraction Challenge

Traditional Computer Vision Limitations

The Deep Learning Solution

Convolutional Neural Networks: Architecture and Operations

The Convolution Operation

Filter Examples and Feature Detection

Convolutional Layer Implementation

Pooling Operations: Spatial Dimension Reduction

Complete CNN Architecture Design

Classical CNN Structure

Modern CNN Best Practices

Advanced CNN Architectures

LeNet-5 (1998): The Pioneer

AlexNet (2012): The Breakthrough

VGGNet: Deep and Simple

Training CNNs: Practical Considerations

Data Preprocessing and Augmentation

Training Configuration

Transfer Learning: Leveraging Pre-trained Models

Modern Computer Vision Applications

Object Detection: YOLO Architecture

Semantic Segmentation: U-Net Architecture

Performance Evaluation and Analysis

Metrics for Computer Vision Tasks

Visualization and Interpretation

Key Insights and Best Practices

Architecture Design Principles

Training Strategies

Modern Trends and Future Directions

Summary and Next Steps

Continue Your Journey

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