Welcome to our comprehensive Dinosaur Image Classification Tutorial!
We’ll learn how use Convolutional Neural Network (CNN) to classify 5 dinosaur categories , based on 200 images :
- Data Preparation: We’ll begin by downloading a curated dataset of dinosaur images, neatly categorized into five distinct classes. You’ll learn how to load and preprocess the data using Python, OpenCV, and Numpy, ensuring it’s perfectly ready for training.
- CNN Architecture: Unravel the secrets of Convolutional Neural Networks (CNNs) as we dive into their structure and discuss the different layers—convolutional, pooling, and fully connected. Learn how these layers work together to extract meaningful features from images.
- Model Training : Â Using Tensorflow and Keras , we will define and train our custom CNN model. We’ll configure the loss function, optimizer, and evaluation metrics to achieve optimal performance during training.
- Evaluation Metrics: We’ll evaluate our trained model using various metrics like accuracy and confusion matrix to measure its efficiency and robustness.
- Predicting New Images: Finally , We put our pre-trained model to the test! We’ll showcase how to use the model to make predictions on fresh, unseen dinosaur images, and witness the magic of AI in action.
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Check out our tutorial here : https://youtu.be/ZhTGcw0C3Dk&list=UULFTiWJJhaH6BviSWKLJUM9sg
Enjoy
Eran
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