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Among the many fields of image recognition, image classification is a technology that has been utilized early on in scenes close to consumers, such as checkout at retail stores and facial recognition on smartphones. In recent years, the development of deep learning technology has enabled higher-precision image classification.
However, many corporate representatives may not have a clear grasp of the specific process for implementing image classification.
In this article, we will introduce the AI models used in image classification, the creation flow, and points to note when implementing it.
For more details on image recognition, please refer to "What is image recognition? Types, mechanisms, AI development processes, case studies and key considerations." reading this along with this article will help you deepen your understanding of image recognition technology.
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【Table of Contents】 |
1. What is Image Classification
Image classification is a technology where a computer recognizes an image and automatically classifies the objects or features appearing in it into predetermined categories. It is a method of image recognition that analyzes and categorizes the entire image.
The utilization of deep learning has improved accuracy, and it is now used in a wide range of fields. Image classification AI models using deep learning extract features within an image and determine the most suitable category based on those features.
2. Examples of Image Classification Applications
Image classification is utilized in scenes such as the following:
- Quality Control and Visual Inspection
- Picking Operations
- Image Search
- Facial Recognition Systems
Each is explained below.
Quality Control and Visual Inspection
Image classification technology is utilized in quality control and visual inspection. While manual inspection by humans was the mainstream in traditional quality control, the introduction of AI-based image classification technology makes it possible to improve inspection accuracy and efficiency.
By taking real-time images of products or parts and having AI analyze them, scratches, abnormalities, and defects can be accurately detected.
AI models used in image classification learn from a massive amount of inspection data through deep learning, enabling them to handle even minute abnormalities. This not only reduces human oversights and improves inspection reliability but also significantly increases inspection speed.
Please also see "What is Visual Inspection? Explaining Methods, Equipment, Operational Methods, and the Pros/Cons and Cautions of AI Inspection!"
Picking Operations
Picking refers to the task of selecting products stored in a warehouse according to shipping instructions. AI-based image classification technology is used to automate picking operations.
For example, a robot equipped with a camera moves through the warehouse and takes real-time images of products on shelves. AI can instantly analyze these images and classify which product is the target for picking.
Image Search
AI-based image classification is also utilized in image search. Unlike traditional text-based search, AI image search looks for similar images or related information based on an image input by the user.
For example, AI can analyze the features of an image a user "liked," compare those features with other images in a database, and present highly relevant images as something the user might like. This makes it easier to search for products or designs that are difficult to describe in text.
Facial Recognition Systems
Facial recognition is a system that identifies individuals by having AI analyze face images taken by a camera and matching them with pre-registered face data. It is used in diverse scenes such as the following:
- Security gates in office buildings
- Unlocking smartphones
- Entry and exit management at airports or large-scale facilities
Image classification utilizing deep learning technology is indispensable in the facial recognition process intended for an unspecified number of users.
3. Comparison of Deep Learning Models Used in Image Classification
Deep learning models are used in image classification. The following three are the models utilized in image classification:
- CNN (Convolutional Neural Networks)
- ViT (Vision Transformers)
- Hybrid Models
We will compare each model.
CNN (Convolutional Neural Networks)
CNN (Convolutional Neural Networks) is a model that automatically extracts important features from image data, enabling high-precision classification.
It is one of the most commonly used deep learning models for image classification over many years. Representative models include the following:
- AlexNet: Simple and easy to understand
- ResNet: Capable of learning extremely deep layers, up to 22 layers
- VGGNet: Highly versatile
- GoogLeNet (Inception): Achieves high precision while suppressing the number of parameters
The characteristic part of a CNN is the "convolutional layer." The convolutional layer captures features such as edges, color changes, and patterns in an image step-by-step, playing the role of understanding the structure within the image.
The major advantage of CNN is that it effectively captures the features of the entire image by sequentially processing local regions rather than analyzing the entire input image at once. It first learns simple features (edges and lines) and identifies more complex patterns and shapes in later layers. This multi-layered image classification allows it to handle tasks such as facial recognition and object recognition.
ViT (Vision Transformers)
ViT (Vision Transformers), developed by Google, is an application of Transformer technology used in natural language processing to image recognition.
Image classification with ViT divides an image into small patches (blocks) and inputs each patch as a token. This allows it to recognize the relationships and structure of the entire image without performing convolution like a CNN.
Hybrid Models
Hybrid models are deep learning models that combine CNN and Transformers, achieving high precision in image classification. By combining the feature extraction technology from CNN convolution with the recognition technology of ViTs, even more precise image classification becomes possible.
CNNs are excellent at capturing fine patterns and edges within an image. On the other hand, ViTs are better at understanding the overall context of the image and the relationships between objects.
4. 6-Step Flow for Creating an Image Classification AI Model
To develop an AI model that enables image classification, it is common to proceed according to the following creation flow:
- Data Preparation
- Annotation
- Model Selection and Construction
- Feature Extraction and Training
- Evaluation and Tuning of the Image Classification Model
- Final Model Evaluation and Deployment
Each is explained below.
1. Data Preparation
The performance of an image classification AI model depends heavily on how much high-quality and diverse image data it has learned. Therefore, the quantity and quality of the image data used for training are crucial.
First, it is necessary to collect a sufficient amount of images of the targets you want to classify. At this time, if the resolution and size of the images are unified, the model training will be smoother.
Furthermore, to avoid data bias, collect the number of images for each category as evenly as possible. For example, in an AI system for classifying whether it's a dog or a cat, if you prepare only images of various dog breeds and have almost no images of cats, the data is biased and the classification accuracy will not improve.
Additionally, it is ideal to collect images taken under various conditions. For example, by including images taken with different backgrounds, angles, and lighting conditions that vary by season, the model will be able to handle diverse situations.
Unclear images degrade learning accuracy, so appropriate preprocessing and cleaning are necessary. Also, by appropriately classifying and organizing the data, it will be easier to proceed with the subsequent annotation work efficiently.
In this way, data preparation must be done carefully, taking time and effort.
Please also see Things to keep in mind when requesting annotation data collection" for data collection cautions.
2. Annotation
Image annotation refers to the task of attaching accurate labels to collected images so that the model can learn. You identify objects within the image and assign corresponding labels.
Through annotation work, training data for the AI model to learn is created. With the training data, the model can learn which images should be classified into which categories.
If the quality of image annotation is low, the model's classification accuracy will decrease. Furthermore, if annotation is performed while the skills of the annotator are insufficient, high-precision image classification cannot be expected.
For more complex tasks, labeling of additional information may be required. For example, if object detection is also needed, the position information of the target object is specified with a bounding box (rectangular frame), and in segmentation, the contour or area of the target object is accurately specified.
Since image classification handles a large amount of image data, the annotation work requires significant effort. To increase the efficiency of annotation work, consider utilizing specialized tools or outsourcing to external parties.
Please also see "Should I outsource to an annotation company or do it in-house? How to choose a company? A comprehensive guide to the benefits of outsourcing!" for how to choose an annotation provider.
3. Model Selection and Construction
Once annotation is complete, select the model that best approaches the company's specific use case for the image classification AI. Especially when only a limited dataset is available, the utilization of transfer learning is effective.
Transfer learning is a method of re-training a model that has been pre-trained on a large-scale dataset to fit a specific task. This can achieve shorter learning times and higher accuracy than building a model from scratch.
For example, using a CNN-based pre-trained model such as ResNet or VGGNet can lead to high performance even when data is limited. Since they have been trained on large-scale datasets like ImageNet, a versatile feature extraction capability can be expected.
By performing transfer learning on those models, it is possible to quickly adapt them to specific classification tasks. By utilizing transfer learning, you can build a model with practical accuracy while significantly reducing computational costs and development time.
Of course, depending on the nature of the task and the amount of available data, approaches other than transfer learning may be suitable.
4. Feature Extraction and Training
Once the AI model for image classification is constructed, feature extraction and training are performed. Features are the parts of an image that show information important for classification and refer to items such as the following:
- Edges
- Shape
- Color
- Pattern
At this stage, the model extracts features from images and becomes able to identify different categories by learning.
5. Evaluation and Tuning of the Image Classification Model
In the evaluation of the image classification model, test data is used against the trained model to measure how accurately it can classify images. Comprehensive judgment of model performance is made using evaluation indicators such as the following:
- Accuracy
- Precision
- Recall
- F1 Score
- AUC-ROC
- Confusion Matrix
It is important to use multiple testing methods, rather than just a single test set, to more accurately evaluate the model's generalization performance.
If problems are found during model evaluation, the next step is tuning. In tuning, classification accuracy is improved by adjusting model parameters or reviewing the quantity and quality of training data.
Furthermore, measures to prevent "overfitting," where the model adapts excessively to specific data and its versatility decreases, are also important. By devising data splitting methods or regularization techniques, you can create a more versatile and reliable model.
By repeatedly performing evaluation and tuning, it is possible to maximize the model's accuracy and build an image classification AI model at a practical level.
6. Final Model Evaluation and Deployment
Final model evaluation and deployment is the critical final phase of a machine learning project. This process consists of the following key steps:
(1) Final Evaluation Using a Test Dataset
This is the stage to finally confirm the performance of the developed model. Model prediction accuracy and generalization performance are evaluated using new data (test dataset) that has not been used for training or validation.
(2) Model Weight Reduction and Optimization (as Needed)
To increase efficiency in the actual operation environment, model weight reduction or optimization may be performed. This includes methods such as the following:
- Model Compression: Transferring the knowledge of a large model to a smaller model
- Quantization: Reducing size by lowering the precision of parameters
- Pruning: Deleting low-importance connections
(3) Performance Verification in Real Environment
The optimized model is tested under conditions close to the actual operation environment. At this stage, the following points are confirmed:
- Processing Speed: Whether it can meet real-time requirements
- Resource Usage: Whether memory and CPU usage are within acceptable ranges
- Stability: Whether it operates without problems even during long-term operation
5. Points to Note When Performing Image Classification with AI
When performing image classification with AI, the following must be noted:
- Set desired accuracy and processing speed
- Establish an environment where high-quality images can be obtained
- High-precision annotation is necessary
Each is explained below.
Set Desired Accuracy and Processing Speed Appropriately
When introducing image classification AI, it is important to clearly set the desired accuracy and processing speed.
Certainly, a model with higher accuracy can classify images more precisely. However, it is not uncommon for the computational cost to increase and the processing speed to decrease accordingly.
On the other hand, it is meaningless if the necessary accuracy cannot be reached because processing speed was prioritized too much. It is important to set a balance according to the content of the business.
For example, in scenes where a large volume of images must be processed in real-time, high-speed processing is required while maintaining a certain level of accuracy. Conversely, in medical image diagnosis or quality control where accuracy is the top priority, a high-precision model is needed while sacrificing speed slightly, as even a small error is not tolerated.
Create an Environment to Obtain High-Quality Images
To realize high-precision image classification with AI, it is necessary to obtain high-quality images for training. Image resolution, clarity, and shooting conditions significantly affect the accuracy of the completed image classification system.
Therefore, it is important to establish an appropriate data collection environment at the data collection stage or even before it. A consistent, high-quality dataset leads directly to improved training efficiency and accuracy of the AI model.
In the shooting environment, it is recommended to unify the stabilization of lighting and camera settings. Blurred images or low-resolution data make it difficult for the AI model to accurately extract features, leading to a decrease in classification accuracy.
However, it is also important to include images taken under various conditions (different angles, lighting conditions, etc.). This improves the model's generalization performance and increases robustness in real environments.
Especially in the manufacturing and visual inspection fields, ensuring a system that can continuously provide a large volume of images while maintaining a certain quality improves the accuracy of the AI model.
High-Precision Annotation Is Necessary
High-precision annotation is indispensable to maximize the accuracy of image classification AI. If annotation work is not accurate, the AI model will learn incorrect information, and classification accuracy will decrease significantly.
Especially when the shape of the target object is complex or detection of minute differences is required, more precise annotation is needed. For example, in fields such as visual inspection and medical imaging, it is necessary to label even very fine parts, and even slight mistakes are not tolerated.
To streamline annotation work, the introduction of specialized annotation tools or outsourcing to reliable specialists is effective. This allows for reducing mistakes due to manual work and creating a more consistent dataset.
6. Summary
Image classification AI realizes high-precision automation and plays a major role in streamlining and quality improvement. As explained in this article, it is possible to develop an optimal image classification AI in-house through processes such as data preparation, model selection/construction, annotation, and evaluation/tuning.
It is important to understand the development steps and construct an image classification AI suitable for the company. Especially since image data collection and annotation determine classification accuracy, consider requesting an annotation specialist company.
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