# MobileNetV1

> 1st version of MobileNet

**Wikidata**: [Q123332027](https://www.wikidata.org/wiki/Q123332027)  
**Source**: https://4ort.xyz/entity/mobilenetv1

## Summary
MobileNetV1 is the first version of the MobileNet family of convolutional neural networks. It is an instance of MobileNet, described by the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications," and is designed as an efficient CNN variant for mobile vision use cases.

## Key Facts
- MobileNetV1 is the first (1st) version of MobileNet.
- Instance of: MobileNet (class of convolutional neural network).
- Subclass of: convolutional neural network, a feed-forward network that learns features via filter (kernel) optimization.
- Alias: MobileNet V1.
- Described by the paper: "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications."
- Followed by / succeeded by: MobileNetV2 (the 2nd version of MobileNet).
- Related to: MobileNet (the broader class/type of convolutional neural network).

## FAQs
### Q: What is MobileNetV1?
A: MobileNetV1 is the first version of the MobileNet family, a convolutional neural network architecture described in the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications."

### Q: How does MobileNetV1 relate to MobileNetV2?
A: MobileNetV1 was succeeded by MobileNetV2, which is the second version of the MobileNet family. MobileNetV2 follows MobileNetV1 in the sequence of MobileNet architectures.

### Q: What type of neural network is MobileNetV1?
A: MobileNetV1 is a convolutional neural network (CNN), a regularized type of feed-forward neural network that learns features via optimization of filters (kernels).

## Why It Matters
MobileNetV1 matters because it establishes the initial MobileNet design line focused on efficient convolutional neural networks for mobile vision applications. As the first version in the MobileNet family, it set the foundation for subsequent iterations (such as MobileNetV2) and the broader adoption of lightweight CNN architectures intended to run effectively on resource-constrained devices. The architecture is documented in the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications," signaling its explicit emphasis on efficiency and mobile deployment. For practitioners and researchers, MobileNetV1 represents a key step in bridging the gap between high-performing visual models and practical on-device inference, forming a base from which later improvements and variants were developed.

## Notable For
- Being the inaugural (1st) version of the MobileNet family.
- Explicitly described in the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications."
- Positioned as an efficient CNN variant intended for mobile vision use-cases.
- Serving as the predecessor to MobileNetV2 (the 2nd version).

## Body
### Classification
- Name: MobileNetV1 (also styled MobileNet V1).
- Instance of: MobileNet (a class/type of convolutional neural network).
- Subclass of: convolutional neural network.
- Wikidata description: "1st version of MobileNet."

### Source / Description
- Primary descriptive source: "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications."
- The source title indicates the architecture's focus on efficiency and mobile vision applications.

### Relationships and Versions
- MobileNetV1 is the first in the sequence of MobileNet architectures.
- Succeeded by MobileNetV2, which is the second version of MobileNet.
- Related entity: MobileNet (the broader class to which MobileNetV1 belongs).

### Parent Category Context
- Parent category: convolutional neural network.
- Convolutional neural networks are feed-forward neural networks that learn features via optimization of filters (kernels). This contextualizes MobileNetV1 as a CNN variant designed within that paradigm.

### Terminology and Aliases
- Common alias: MobileNet V1.
- Often referenced in literature as the initial MobileNet architecture described in the MobileNets paper.