Landmark 6 stops to get here · leads to 3

Image Classification

Assigning a single label or category to an entire image, a fundamental computer vision task.

Your route here

6 stops · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Supervised Learning ✓ understood

    Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.

  3. Classification ✓ understood

    A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam.

  4. Neural Network ✓ understood

    A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

  5. Deep Learning ✓ understood

    A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.

  6. Computer Vision ✓ understood

    The field of AI that gets computers to extract meaning from images and video: what is in them, where it is, and how it moves.

  7. Image Classification · you are here ✓ understood

Picture it

  1. 01 Input image A grid of pixel values, e.g. 224×224×3
  2. 02 Feature extractor A CNN or vision transformer
  3. 03 Class scores One logit per category
  4. 04 Softmax Scores become probabilities
  5. 05 One label e.g. "cat" at 0.92
Notice the whole image collapses to a single label: the model never says where the object is, only what the image is.

Where it sits

Before this

Computer Vision Classification
Image Classification

Leads to

ImageNet Transfer Learning in Vision Medical Diagnosis

Explore nearby

Neural Networks Convolutional Neural Network A neural network that scans images with small learned filters, reusing the same weights at every position to build up from edges to whole objects. Vision & Multimodal ImageNet A large-scale dataset of 14M images in 20K categories, historically used as the benchmark for image classification models. Vision & Multimodal ResNet Residual Network - a CNN architecture using skip connections to enable training of very deep networks (up to 1000+ layers). Vision & Multimodal Vision Transformer Applying the transformer architecture to computer vision by treating image patches as tokens, achieving state-of-the-art results.

In the research

All papers →

2 papers that build on Image Classification .