lucid.models¶
The lucid.models package provides a collection of predefined neural network architectures that are ready to use for various tasks, such as image classification and feature extraction. These models are designed to demonstrate key deep learning concepts while leveraging the modular and educational nature of the lucid framework.
Computer Vision¶
Computer Vision (CV) is a field of artificial intelligence that enables machines to interpret and understand visual information from the world, such as images and videos. It involves teaching computers to process, analyze, and make sense of visual data in a way similar to human vision.
Task |
Description |
Docs |
|---|---|---|
Image classification |
Image classification is a key task in computer vision where a model assigns labels to images based on their content. It processes the image through layers to extract features and predict the most likely class. |
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Object Detection |
Object detection is a computer vision task that identifies and classifies multiple objects within an image. It assigns labels and draws bounding boxes around each detected object, combining localization with classification. |
Natural Language Processing¶
Natural Language Processing (NLP) is a field of artificial intelligence that enables computers to understand, interpret, and generate human language. It combines linguistics and machine learning to process text or speech, allowing models to perform tasks like translation, summarization, and sentiment analysis.
Task |
Description |
Docs |
|---|---|---|
Sequence-to-Sequence |
A sequence-to-sequence model is a type of neural network architecture used to transform one sequence into another, such as translating a sentence from one language to another. |