> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Pretrained Models

> Using pretrained neural networks for feature extraction in anomaly detection

## Overview

Pretrained models provide powerful feature extraction capabilities for anomaly detection without requiring domain-specific training. These models have been trained on large-scale datasets and can extract rich, hierarchical representations from images.

## Available Models

<CardGroup cols={2}>
  <Card title="ResNet-50 (CNN)" icon="layer-group" href="./cnn">
    Classic convolutional neural network pretrained on ImageNet
  </Card>

  <Card title="CLIP" icon="images" href="./clip">
    Vision-language model with zero-shot capabilities
  </Card>
</CardGroup>

## Why Pretrained Models?

1. **No Domain-Specific Training Required**: Features can be extracted immediately without training on the target dataset
2. **Rich Representations**: Models learn hierarchical features from edges to complex patterns
3. **Transfer Learning**: Knowledge from large datasets transfers to specialized domains
4. **Computational Efficiency**: No expensive training phase required for feature extraction

***

<Callout icon="pen-to-square" iconType="regular">
  [Edit this page on GitHub](https://github.com/aegean-ai/eaia/edit/main/src/products/applications/anomaly-detection/manufacturing/unsupervised-learning-models/pretrained/index.mdx) or [file an issue](https://github.com/aegean-ai/eaia/issues/new/choose).
</Callout>