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Machine learning for image classification
Machine learning for image classification assigns one label or score to a whole image, such as a product category, a plant species or a pass or fail on a part. Modern classifiers fine-tune a pretrained network, so a modest labelled set is often enough.
How machine learning is used for image classification
Identifying species from photos
A classifier maps a photo to a species label, which lets ecologists process camera-trap images and lets the public identify plants and animals. The model is usually a pretrained CNN or vision transformer fine-tuned on labelled photos.
Real exampleSnapshot Serengeti camera-trap studyResearchers trained deep convolutional networks to identify, count and describe the behaviour of 48 species in the 3.2-million-image Snapshot Serengeti dataset.
Plant identification apps
Users photograph a leaf or flower and the app returns a ranked list of likely species. Observations shared by users become new training data for the next model.
Real examplePl@ntNetPl@ntNet is a citizen science project available as an app that identifies plants from pictures and collects observations on plant biodiversity.
Classifying scientific images
Scientists use classifiers to sort very large image collections that experts or volunteers cannot label by hand. The model learns from an expert or crowd-labelled subset and labels the rest.
Real exampleGalaxy morphology with CNNsA rotation-invariant convolutional network developed for the Galaxy Challenge reproduced Galaxy Zoo volunteers' consensus classifications of galaxy shapes with high accuracy on images where volunteers agreed.
Medical image classification in research
Researchers train classifiers to separate image categories such as benign and malignant skin lesions, then compare them with specialists on held-out cases. This is research evidence, not a diagnostic tool.
Real exampleDermatologist-level skin lesion classificationA Stanford team trained a single CNN on clinical images of skin lesions and reported performance on par with board-certified dermatologists on the tested classification tasks.
Can you learn machine learning for image classification?
Yes. Image classification is the most common first deep learning project, and fine-tuning a pretrained model needs only basic Python and some machine learning foundations. Learn Python, then model evaluation, then CNNs and transfer learning.
- Python and image loadingRead image folders, resize and normalise images, and build training, validation and test splits.Learn it in Python
- Classification fundamentalsBaselines, overfitting, accuracy, per-class recall and confusion matrices tell you whether a classifier is useful.Learn it in Machine Learning
- Convolutional networksUnderstand convolution, pooling and softmax outputs by training a small CNN from scratch on a dataset such as CIFAR-10.Learn it in Deep Learning
- Transfer learning and augmentationFine-tune a pretrained ResNet or vision transformer with flips, crops and colour changes, which works with far fewer labelled images.Learn it in Deep Learning
Moving into image classification work
If you already work in the field
If you already sort or grade images by eye, as a lab technician, inspector or ecologist, you know the classes and the hard cases. Learn Python and fine-tuning, then start with a labelled sample of your own images and compare the model with your colleagues' labels.
If you come from data science or ML
If you know tabular models, add image pipelines, augmentation and pretrained backbones. Spend time with domain experts on label definitions, because unclear classes cause more errors than model choice.
Roles that do this workComputer vision engineerMachine learning engineerData scientist, imagingApplied scientist
Tools you will use
| Tool | What you use it for |
|---|---|
| torchvision | Load pretrained image classifiers, datasets and transforms, and fine-tune them in PyTorch. |
| timm | Compare many pretrained image backbones for classification with consistent training scripts. |
| Keras | Build and fine-tune image classifiers with a high-level API on TensorFlow, JAX or PyTorch. |
| fastai | Fine-tune a pretrained classifier in a few lines and inspect the images it gets most wrong. |
| scikit-learn | Fit simple baselines on image features and compute accuracy, confusion matrices and per-class reports. |
Public data to practise on
- CIFAR-10: Small colour images in ten everyday classes, ideal for comparing a baseline with a convolutional network quickly.
- Imagenette: A ten-class subset of ImageNet at several image sizes, for fast transfer-learning experiments.
- Oxford 102 Flowers: Photos of 102 flower categories with fine differences, for testing classifiers on similar-looking classes.
Your first project
Download CIFAR-10, fine-tune a pretrained ResNet-18 to predict the ten classes, and compare it with logistic regression on raw pixels using test accuracy and a confusion matrix.
Common questions
What is the difference between image classification and object detection?
Classification gives one label, or a set of labels, for the whole image. Detection also finds where each object is and draws a box around it.
Which model should I start with for image classification?
A pretrained ResNet-18 or a small vision transformer from torchvision or timm is a good start. It trains quickly and gives a strong baseline before you try larger models.
How do I handle classes with very few images?
Use a pretrained model, augment the rare classes, and report per-class recall rather than overall accuracy. Collecting more examples of the rare classes usually helps most.
Can I train an image classifier without coding?
Some no-code tools train simple classifiers from uploaded folders. Learning Python gives you control over data splits, evaluation and deployment, which matters once results are used for decisions.
Courses for these skills
- Deep Learning with PyTorchStudy CNNs and object detection in the Computer Vision syllabus.Syllabus available · lessons open as produced
- Python for AI and Machine LearningBuild the basic programming skills used to handle image records.33 published lessons · all planned lessons open
- Statistics and Probability for Machine LearningAssess class imbalance and uncertainty in measured performance.41 published lessons · all planned lessons open
- Data Tools: NumPy, pandas, Matplotlib and scikit-learnKeep annotation coordinates and image identifiers aligned.23 published lessons · all planned lessons open
New to programming or maths? Start with Python, or follow the machine learning path in order. Published lessons are free to read; compare Free and Pro.