ニューラルネットワークによる画像分類：初心者向けガイド

Image classification is the task of assigning a label to an entire image. It's one of the foundational tasks in deep learning.

How Image Classification Works

1. Input: An image is represented as a grid of pixel values

2. Feature extraction: Convolutional layers detect patterns (edges, textures, shapes)

3. Classification: Fully connected layers map features to class probabilities

4. Output: The class with highest probability is the prediction

Vision Transformers (ViT)

Modern classifiers use Vision Transformers instead of CNNs:

Accuracy and Limitations

Classify any image with our Image Classifier using the ViT model running entirely in your browser.

Once you know what is in the image, use our Image Captioner to generate a natural-language description of the scene.