Accelerating AI with Continuous Delivery: Building and Training a Model with Keggle
Introduction
Welcome to the second part of our video series on accelerating AI with continuous delivery. In this part, we will be focusing on building and training a model using the online platform Keggle. Data plays a crucial role in model training, and in our case, we will be detecting cats and dogs. Having a high-quality dataset is essential for successful model training, and we will be utilizing the Oxford Free Pets dataset, which contains over 7,000 images of cats and dogs.
Model Training Process
Before we dive into the details of model training, it’s important to understand how the process works. We begin by splitting our data into two parts: training and validation. This division ensures that we have a separate set of data to test the model’s performance accurately. The recommended ratio for splitting the data is 80% for training and 20% for validation. This allows us to evaluate the model’s success with a degree of accuracy above 95%.
Keggle Platform
To get started with model training on Keggle, you first need to create an account and verify it by setting up phone verification. This step is crucial to proceed with running code on the platform. Once your account is verified, you can access a pre-prepared notebook for the cats and dogs model training example. By copying and fine-tuning this model, you can test it out on your own dataset.
Model Architecture
In our model, we are using the ResNet-34 architecture, which is pre-trained on a dataset with 100,000 images across 200 classes. We will fine-tune this model with the Oxford Free Pets dataset, which contains 7,000 images of dogs and cats. The data labeling process is essential for evaluating the model’s predictions, with cat images starting with uppercase names and dog images with lowercase names.
Data Preparation
The data preparation phase involves loading the training and validation datasets from the images provided. By running the code in the notebook, you can visualize the results and check the accuracy of the model’s predictions. The fine-tuning process involves specializing the model to correctly identify cats and dogs, resulting in a validation of the model’s performance.
Evaluation Metrics
To evaluate the model’s performance, we utilize metrics such as the confusion matrix. This matrix helps us identify false positives and true negatives, guiding us in refining the model for better accuracy. By plotting the top losses, we can pinpoint images that were incorrectly predicted and fine-tune the model accordingly.
Testing the Model
In the final part of the notebook, we test the model using images from the internet to determine the probability of the image being a cat or a dog. By running this code, we can assess the model’s accuracy on external images and make adjustments as needed.
Conclusion
The Keggle platform offers a convenient way to train and test AI models online, providing instant results and the ability to fine-tune the model for improved performance. In the next part of this series, we will be exploring how to run the code locally using tools like Visual Studio Code. Stay tuned for more insights into accelerating AI with continuous delivery. Make sure to subscribe to our channel for future updates. Thank you for watching!
