What Is Image Classification?

Image classification is a fundamental task in computer vision where a system assigns a label to an entire image, identifying what is present within it. For example, the system might determine whether an image contains a cat, a car, or a tree. This process involves analyzing the visual content of the image and categorizing it based on predefined classes.

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How Image Classification Works

Image classification algorithms work by analyzing the features of an image and matching them to known categories. These algorithms are typically trained on large datasets of labeled images, like the popular ImageNet dataset [1], which enables them to learn the characteristics of each category. Once trained, the algorithm can classify new, unseen images by comparing them to the patterns and features it has learned.

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Here is a streamlined overview of the image classification process:

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1. Data collection and preparation

Gather a diverse set of labeled images relevant to your classification task, such as various animal types (e.g., "cat", "dog", "bird"). Preprocess these images by resizing them uniformly, normalizing pixel values, and augmenting them through modifications like rotations and flips. This ensures the model learns robust features from the images.

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2. Model training

Feed the processed images into a learning algorithm, which learns to associate features with labels by adjusting its parameters to minimize prediction errors. The data is split into training and validation sets to ensure the model generalizes well and does not just memorize the training data.

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3. Validation and fine-tuning

Validate the model's performance using a separate dataset. Compare its predictions to the actual labels and adjust parameters to improve accuracy. Fine-tuning helps the model perform well on both seen and unseen images by refining the learning process (OpenCV).

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4. Inference and deployment

Deploy the model to classify new images. During inference, the model applies learned features to predict class labels for new images.

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Confidence Levels in Classification

When an image classification system analyzes an image, it doesn't just provide a single result; it often returns multiple possible categories along with confidence levels. These confidence levels indicate how certain the system is that the image belongs to each category. For instance, in a cat species classifier, you might get a result like this:

1. Persian Cat (97.6%)

2. Turkish Angora (2.3%)

3. Scottish Fold (0.1%)

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In this example, the classifier has identified three possible categories for the cat in the image. The highest confidence level is for "Persian Cat" at 97.6%, which means the system is highly certain that the image is of a Persian Cat. In practical applications, the category with the highest confidence level is usually taken as the final classification result.

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Types of Image Classification

Image classification can be broadly categorized into three types, each varying in complexity and application. These classifications help to understand the scope and capabilities of image classifiers in different contexts:

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1. Binary Classification

Binary classification is the simplest form of image classification where the model decides between two possible outcomes. This type of classification is akin to answering a yes/no question about an image. For example, determining whether an image contains a cat or not is a typical binary classification task. This straightforward approach is useful for scenarios where only two distinct classes exist, making it easy to implement and interpret.

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Example Applications:

NSFW Detection: An image classifier could ensure NSFW content cannot be displayed, making it particularly valuable when deploying a diffusion model in production environments to ensure content appropriateness and compliance.
Disease Detection: Identifying the presence or absence of a specific disease in medical images.
Safety Systems: Detecting if a safety hazard, like a fire or a specific object, is present or not.

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2. Multi-Class Classification

Multi-class classification involves categorizing images into one of three or more classes. Each image belongs exclusively to one class among the multiple available. For instance, classifying images of various animal species where each image is assigned to a specific species like "lion," "tiger," or "bear" is an example of multi-class classification.

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Example Applications:

Animal Species Identification: Categorizing images into different animal species.
Object Detection in Retail: Identifying different products in a retail store.
Facial Recognition: Classifying images into various known individuals in a facial recognition system.

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3. Multi-Label Classification

In multi-label classification, an image can be assigned multiple labels simultaneously. This is suitable for scenarios where categories are not mutually exclusive and an image can belong to several classes at once. For example, an image could be tagged with multiple labels like "sunset," "beach," and "vacation," indicating that it contains elements of all these categories.

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Example Applications:

Content Tagging: Automatically tagging images in a photo library with multiple relevant keywords.
Medical Imaging: Identifying multiple diseases or conditions from a single medical image.
Environmental Monitoring: Classifying various attributes like "forest," "river," and "pollution" in an environmental monitoring system.

Each type of image classification has unique advantages and is suited for different use cases, enabling a wide range of applications across industries.

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Top image classification models in 2024

Image classification is a core task in computer vision, and over the years, various models have achieved state-of-the-art performance. Here are some of the best image classification models as of 2024:

Type	Notable Models	Description	Strengths
Vision Transformers (ViTs)	ViT, Swin Transformer, DeiT	Transformers for image classification; process image patches as tokens.	Capture long-range dependencies; great on large datasets.
Convolutional Neural Networks (CNNs)	ResNet, DenseNet, EfficientNet, MobileNet, YOLOv8	Use convolutional layers to capture spatial hierarchies in images.	Effective for spatial data; optimized for many tasks.
Hybrid Models	ConvNext, CoAtNet	Combine CNNs and transformers for local and global features.	Balance local and global context understanding.
Self-Supervised Learning Models	SimCLR, BYOL, DINO	Learn from unlabeled data using pseudo-labels or tasks.	Require less labeled data; competitive performance.
Ensemble Models	ResNeXt, BiT	Combine multiple models for better performance.	Reduce prediction variance, improve robustness.
Few-Shot and Zero-Shot Learning	CLIP, DINO, MAML	Generalize from few or no examples using transfer learning.	Adapt to new tasks with minimal data.

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Performance and Use Cases

Vision Transformers: Excellent for tasks where understanding global context is crucial, such as scene classification.
CNNs: Well-suited for tasks requiring detailed local feature extraction, like object recognition.
Hybrid Models: Ideal for complex tasks requiring both local and global understanding, such as medical image analysis.
Self-Supervised Learning Models: Beneficial for scenarios with limited labeled data, such as satellite imagery analysis.
Ensemble Models: Effective for improving robustness and accuracy in challenging datasets like ImageNet.‍
Few-Shot and Zero-Shot Learning Models: Useful for adapting to new tasks quickly, beneficial in dynamic environments like real-time image classification.

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Easily run Image Classification models

The Ikomia API simplifies the process of image classification, requiring minimal coding effort.

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Setup

Start by setting up a virtual environment [2] and then install the Ikomia API within it for an optimized workflow:


pip install ikomia

Example: Run ResNet with a few lines of code

You can also directly charge the notebook we have prepared.

Go to notebook

Go to Colab


from ikomia.dataprocess.workflow import Workflow
from ikomia.utils import ik
from ikomia.utils.displayIO import display


# Init your workflow
wf = Workflow()    

# Add ResNet to the workflow
resnet = wf.add_task(ik.infer_torchvision_resnet(model_name="resnet50"), auto_connect=True)

# Run on your image  
# wf.run_on(path="path/to/your/image.png")
wf.run_on(url="https://github.com/Ikomia-dev/notebooks/blob/main/examples/img/img_porsche.jpg?raw=true")

# Inspect your results
display(resnet.get_image_with_graphics())

Explore the list of classification algorithms available in the Ikomia API. This includes the widely-used PyTorch Image Models (TIMM) library, which features over 300 pre-trained, state-of-the-art image classification models.

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