A simple way of implementing CNN architectures. The problem is to build a classification model.

Tutorial from: freeCodeCamp, Youtube

Main requirements

• tensorflow 2.x
• matplotlib
• keras (available in the Tensorflow package)

Version

Python 3.9.7

Tensorflow 2.6.0

Environment

Anaconda, Windows 10

How to use

In the terminal, run python train.py to train my model, run python train.py -p to train a model created from another pre-trained model (-p stands for the pretrained flag)

Datasets

CIFAR-10 (https://www.cs.toronto.edu/~kriz/cifar.html). It contains 60,000 32x32 color images with 6000 images of each class.

The labels in this dataset are the following:

• Airplane
• Automobile
• Bird
• Cat
• Deer
• Dog
• Frog
• Horse
• Ship
• Truck

Documentation:

1. "Convolutional Neural Network (CNN)  :   TensorFlow Core." TensorFlow, www.tensorflow.org/tutorials/images/cnn.
2. "Transfer Learning with a Pretrained ConvNet  :   TensorFlow Core." TensorFlow, www.tensorflow.org/tutorials/images/transfer_learning.
3. Chollet François. Deep Learning with Python. Manning Publications Co., 2018.
4. Object Detection, https://github.com/tensorflow/models/tree/master/research/object_detection