Intermediate
Ai
Machine learning
Image recognition
AI Image Training
This project will focus on training a machine learning model to accurately classify images using deep learning techniques
In this project, we will be using a combination of convolutional neural networks (CNNs) and transfer learning to train a model to recognize and classify different types of images. We will start by gathering a large dataset of images, and then use this dataset to train and fine-tune our model.
Project Checklist
- Gather a dataset of images for training and testing
- Preprocess and clean the data
- Split the data into training and testing sets
- Train a CNN model on the training data
- Fine-tune the model using transfer learning
- Evaluate the model on the testing data
Bonus Project Checklist Items
- Implement data augmentation to improve model performance
- Experiment with different CNN architectures and hyperparameters
- Apply the trained model to real-world image classification tasks
Hint
To get started, you can use the following code snippet to download and preprocess the data:import tensorflow as tf
# Download and extract the data
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
# Preprocess the data
x_train = x_train / 255.0
x_test = x_test / 255.0