MobileNet V2 1.0 224
| Property | Value |
|---|---|
| Parameter Count | 3.54M |
| License | Other |
| Paper | View Paper |
| Input Resolution | 224x224 |
| Tensor Type | F32 |
What is mobilenet_v2_1.0_224?
MobileNet V2 is a lightweight computer vision model specifically designed for mobile and edge devices. Created by Google, this model represents a significant advancement in efficient deep learning architectures, utilizing 3.54M parameters to perform image classification tasks on the ImageNet-1k dataset.
Implementation Details
The model employs inverted residuals and linear bottleneck architecture, optimized for mobile devices while maintaining high accuracy. It processes images at 224x224 resolution with a depth multiplier of 1.0, representing the full-width version of the architecture.
- Optimized for mobile deployment with minimal computational overhead
- Implements innovative inverted residual structure
- Supports PyTorch framework
- Uses F32 tensor type for computations
Core Capabilities
- Image classification across 1000 ImageNet classes plus background class
- Efficient feature extraction for transfer learning
- Mobile-optimized inference
- Support for various computer vision tasks through transfer learning
Frequently Asked Questions
Q: What makes this model unique?
MobileNet V2 stands out for its exceptional balance between model size and performance, specifically designed for mobile applications. Its inverted residual structure and linear bottlenecks allow for efficient computation while maintaining good accuracy.
Q: What are the recommended use cases?
The model is ideal for mobile and edge device deployments requiring image classification capabilities. It's particularly suitable for applications where computational resources are limited but real-time performance is necessary, such as mobile apps, IoT devices, and embedded systems.
