Image Classification
Learn to perform image classification using Hugging Face models.
We'll cover the following...
- What is image classification?
- From CNNs to vision transformers
- Modern transformer vision models
- Using the image classification pipeline
- Preprocessing
- Exploring modern models
- Multi-class classification
- Using custom images & batch inference
- Error handling
- Datasets
- Fine-tuning your own dataset
- Try it yourself
- Summary
Image classification is one of the foundational tasks in computer vision.
The goal is to assign labels to images, such as identifying a dog, car, or tumor in a medical scan. In this lesson, we’ll learn how image classification works using transformer-based vision models, explore Hugging Face pipelines, and build an intuition you can expand on in later lessons (object detection, segmentation, embeddings, etc.).
What is image classification?
Image classification is the task of teaching machines to recognize the content of an image and assign it one or more meaningful labels.
Instead of simply matching pixel patterns, modern models learn features such as edges, textures, shapes, and even semantic relationships. This allows them to distinguish between similar objects, interpret medical scans, or understand real-world scenes with impressive accuracy.
Image classification powers a wide range of real-world applications, from healthcare to e-commerce. It is used in:
Detecting diabetic retinopathy from retina scans.
Identifying food items in mobile apps for calorie tracking.
Automatically tagging product images in e-commerce catalogs.
Diagnosing diseases in medical images such as X-rays or skin lesions.
Recognizing road signs and pedestrians in autonomous vehicles.
For nearly a decade, convolutional neural networks (CNNs) led computer vision because they mimic how humans detect visual features.
In 2020, the field shifted dramatically with the introduction of the Vision Transformer (ViT), which replaced local convolutional filters with attention mechanisms that analyze entire images globally. This opened the door to more scalable training and better performance when paired with large datasets.
Modern state-of-the-art models
ViT (Google): Pioneered the transformer approach for images
DeiT (Meta): Efficient ViT trained without massive private datasets
Swin Transformer (Microsoft): Hierarchical transformer that captures local detail
ConvNeXt (Meta): A next-generation CNN redesigned to compete with ViT
CLIP (OpenAI): Learns from text-image pairs and powers multimodal AI systems
Fun fact: Transformers in vision were inspired by models originally built for language tasks like BERT and GPT.
From CNNs to vision transformers
For years, Convolutional Neural Networks (CNNs) dominated computer vision.
They use local convolution filters to detect edges, textures, and shapes in a hierarchical manner. This makes them excellent at recognizing objects and patterns in images. However, CNNs struggle with ...