Master Image Classification with Pre-Trained Models

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Master Image Classification with Pre-Trained Models | AI Platform Guide

Table of Contents

Introduction
Preparing the Data
Creating the Classifier
Training the Model
Deploying the Model
testing the Model
Conclusion

Introduction

In this article, we will explore how to use the platform for AI, also known as Pi, to train a machine learning model. Specifically, we will focus on a pre-trained model that is already customized to perform a specific task. We will discuss the advantages of using pre-trained models and how they require less training data compared to traditional models. As an example, we will train a system to classify cats and dogs using a small dataset.

Preparing the Data

Before we can start training our model, we need to prepare the training data. In this case, we have 10 images of cats and 10 images of dogs. Normally, a large dataset would be required for accurate results, but pre-trained image classifiers can still perform well with small sets of images. We will import the data into the platform and organize it into separate folders for cats and dogs.

Creating the Classifier

To create a classifier, we need to access the machine learning platform for AI. We will navigate to the platform's console and select the region where we want to create the classifier. In this case, we have chosen Shanghai. Within the platform, there are three pre-trained models available: OCR for recognizing text, image classification for classifying images, and object detection for detecting specific objects. We will choose the image classification model for our purpose.

Training the Model

With the classifier created, we can now proceed to the training step. We will import the labeled data from the OSS bucket containing our cat and dog images. The data import process will automatically pre-tag the images based on their respective folders. Once the data is imported, we can review the labeled images to ensure accuracy. After confirming the labeled data, we will create a new training task and select the labeled images for training. We will choose the high-performance option for faster training.

Deploying the Model

Once the model training is complete, we can deploy the model. We can access the model's accuracy score, loss, and size. Additionally, we have the option to deploy the model through the PI ease service, which allows for easy deployment of machine learning models. In this case, we will scan a QR code using Alipay on our mobile phone to test the model.

Testing the Model

Using Alipay, we will scan the QR code and point our camera at various images of cats and dogs. The model will provide a rating indicating the confidence of whether the object is a cat or a dog. We will test the model with different images and evaluate its accuracy. We may encounter some errors, but overall, the pre-trained model performs well.

Conclusion

In conclusion, pre-trained models offer a convenient way to perform machine learning tasks with less training data. In this article, we have demonstrated how to train and deploy a pre-trained image classifier using the platform for AI. Although our example used a small dataset, the principles can be applied to larger datasets with even better accuracy. Incorporating pre-trained models into your AI projects can save time and resources while achieving reliable results.

Pros:

Less training data required
Faster training process
Convenient deployment options

Cons:

Possible errors in classification
Limited customization options

Highlights

Utilizing the platform for AI (PI) to train a pre-trained model for image classification.
The advantages of using pre-trained models and their ability to work with small sets of training data.
Importing and organizing the training data into separate categories.
Creating and training a classifier using the high-performance option.
Deploying the trained model through PI ease for easy access and usage.
Testing the model's accuracy and evaluating its performance.
The benefits and drawbacks of using pre-trained models in AI projects.

FAQ

Q: Can I use the platform for AI (PI) for other tasks besides image classification? A: Yes, the platform offers other pre-trained models for tasks such as text recognition (OCR) and object detection.

Q: How accurate is the pre-trained image classifier with a small dataset? A: The pre-trained image classifier can still perform well with small sets of images, but its accuracy may vary depending on the dataset and specific images.

Q: Can I customize the pre-trained image classifier to recognize other objects or categories? A: The pre-trained image classifier has limitations in terms of customization. It is best suited for the categories it was trained on.

Q: Are there any additional costs for using the platform for AI and deploying the models? A: The platform for AI is currently in public beta, which means there are no additional costs for training and deploying models.