CS5720 - Image Classification: End-to-End Pipeline

Data Collection & Preparation

Gathering, organizing, and preprocessing image data for training

Data Sources

• Public datasets (ImageNet, CIFAR)
• Web scraping with proper licensing
• Custom photography/collection
• Synthetic data generation

Preprocessing Steps

• Resize to consistent dimensions
• Normalize pixel values (0-1 or -1,1)
• Data augmentation techniques
• Train/validation/test splits

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Model Architecture Selection

Choose or design a neural network architecture suitable for your task

Popular Architectures

• ResNet: Skip connections for deep networks
• EfficientNet: Balanced scaling
• Vision Transformer: Attention-based
• MobileNet: Lightweight for mobile

# PyTorch model selection
import torchvision.models as models
model = models.resnet50(pretrained=True)
model.fc = nn.Linear(2048, num_classes)
                        

Design Considerations

• Number of classes in your dataset
• Available computational resources
• Speed vs accuracy requirements
• Transfer learning opportunities

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Training & Optimization

Train the model using your prepared data and optimize hyperparameters

Training Setup

• Loss function (CrossEntropyLoss)
• Optimizer (Adam, SGD)
• Learning rate scheduling
• Batch size configuration

# Training loop
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=7)
criterion = nn.CrossEntropyLoss()
                        

Monitoring

• Track training/validation loss
• Monitor accuracy metrics
• Early stopping to prevent overfitting
• Visualize training progress

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Evaluation & Testing

Assess model performance on unseen data and analyze results

Evaluation Metrics

• Accuracy: Overall correctness
• Precision/Recall per class
• F1-score: Balanced metric
• Confusion matrix analysis

# Evaluation
from sklearn.metrics import classification_report
y_pred = model.predict(X_test)
print(classification_report(y_true, y_pred))
                        

Analysis

• Identify misclassified examples
• Analyze per-class performance
• Check for bias or overfitting
• Compare with baseline models

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Deployment & Inference

Deploy the trained model for real-world use and handle inference

Deployment Options

• Cloud APIs (AWS, Google Cloud)
• Edge devices (mobile, IoT)
• Web applications (Flask, FastAPI)
• Batch processing systems

# Simple API endpoint
from flask import Flask, request
app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    image = preprocess(request.files['image'])
    prediction = model(image)
    return jsonify({'class': prediction})
                        

Production Considerations

• Model optimization (quantization)
• Monitoring and logging
• Error handling and fallbacks
• Performance and scalability

Popular Classification Models Comparison

ResNet-50

76.1%

Deep residual learning with skip connections. Excellent baseline for most tasks.

EfficientNet-B0

77.3%

Balanced scaling of depth, width, and resolution. Great efficiency.

Vision Transformer

81.8%

Attention-based architecture. State-of-the-art with large datasets.

MobileNet-V3

75.2%

Optimized for mobile devices. Fast inference with reasonable accuracy.

Image Classification: End-to-End Pipeline

Popular Classification Models Comparison

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