Image Classification

Image classification is the task of assigning a label to an entire image. It's one of the most fundamental computer vision tasks and powers applications from medical diagnosis to autonomous vehicles.

Goal: Given an image, predict which category it belongs to (e.g., cat, dog, car, airplane).

How It Works

Input Image

Raw pixels (e.g., 224×224×3 for RGB)

Feature Extraction

CNN layers detect edges, textures, patterns

Classification

Fully connected layers predict class probabilities

Output

Class label + confidence score

Popular Architectures

ResNet

2015

Residual connections allow training very deep networks (50-152 layers).

Use case: General-purpose image classification, transfer learning

EfficientNet

2019

Balances depth, width, and resolution for optimal efficiency.

Use case: Mobile/edge devices, resource-constrained environments

Vision Transformer (ViT)

2020

Applies transformer architecture to image patches instead of CNNs.

Use case: Large-scale datasets, state-of-the-art accuracy

Simple Classification Example

Using a pre-trained model with TensorFlow/Keras.

python

Output:

Click "Run Code" to see output

Training a Custom Classifier

Fine-tune a pre-trained model for your own classes.

python

Output:

Click "Run Code" to see output

Real-World Applications

🏥

Medical Imaging

Detect diseases from X-rays, MRIs, CT scans

🚗

Autonomous Vehicles

Recognize pedestrians, traffic signs, obstacles

🔒

Security

Face recognition, anomaly detection

🌾

Agriculture

Crop disease detection, yield prediction

🏭

Manufacturing

Quality control, defect detection

🛍️

E-Commerce

Visual search, product categorization

Best Practices

▹Use transfer learning—don't train from scratch

▹Augment data (rotation, flip, zoom) to prevent overfitting

▹Normalize pixel values (0-1 or standardize)

▹Use appropriate image size (224×224 is common)

▹Monitor validation accuracy to detect overfitting

▹Try different architectures for your specific task

Key Takeaway: Modern image classification relies on CNNs and transfer learning. Pre-trained models like ResNet can be fine-tuned for custom tasks with relatively small datasets.