Convolutional Neural Network for Image Classification

Convolutional Neural Network for Image Classification

A Convolutional Neural Network (CNN) is a class of deep neural networks, most commonly applied to analyzing visual imagery. This article describes a specific implementation of a CNN using the TensorFlow and Keras libraries to classify images from the CIFAR-10 dataset.

Overview

The model described herein is designed to classify low-resolution color images ($32\times 32$ pixels) into one of ten distinct classes (e.g., airplanes, automobiles, birds, cats). The implementation utilizes a sequential architecture consisting of a convolutional base for feature extraction followed by a dense network for classification.

Dataset

The system is trained on the CIFAR-10 dataset, which consists of 60,000 $32 \times 32$ color images in 10 classes, with 6,000 images per class.

• Training set: 50,000 images
• Test set: 10,000 images
• Preprocessing: Pixel values are normalized to the range [0, 1] by dividing by 255.0 to accelerate convergence during gradient descent.

Dataset example used for CNN image classification

Network Architecture

The architecture follows a sequential pattern: Conv2D $\rightarrow$ MaxPooling $\rightarrow$ Dense. The specific layer configuration and parameter counts are detailed below:

Model: "sequential"

Layer (type)	Output Shape	Param #
conv2d (Conv2D)	(None, 30, 30, 32)	896
max_pooling2d (MaxPooling2D)	(None, 15, 15, 32)	0
conv2d_1 (Conv2D)	(None, 13, 13, 64)	18,496
max_pooling2d_1 (MaxPooling2D)	(None, 6, 6, 64)	0
conv2d_2 (Conv2D)	(None, 4, 4, 64)	36,928
flatten (Flatten)	(None, 1024)	0
dense (Dense)	(None, 64)	65,600
dense_1 (Dense)	(None, 10)	650
Total params: 122,570 (478.79 KB)
Trainable params: 122,570 (478.79 KB)
Non-trainable params: 0 (0.00 B)

Implementation Details

Training Configuration

The model is compiled with the following hyperparameters:

• Optimizer: Adam (Adaptive Moment Estimation).
• Loss Function: Sparse Categorical Crossentropy (from_logits=True).
• Metrics: Accuracy.
• Epochs: 10 iterations over the entire dataset.

Performance

Upon training for 10 epochs, the model typically achieves:

• Training Accuracy: High (variable based on initialization).
• Test Accuracy: Approximately 70% – 75%.
• Overfitting: A divergence between training accuracy and validation accuracy suggests the model may memorize training data. Techniques such as Dropout or Data Augmentation are recommended to mitigate this.

Result

Inference on Custom Images

To test the model on high-quality external images, the input must be resized to match the network's input constraints ($32 \times 32$ pixels).

img = image.load_img(path, target_size=(32, 32))
img_array = image.img_to_array(img) / 255.0
predictions = model.predict(img_array)