MobileNet V1 0.75 192
| Property | Value |
|---|---|
| Author | |
| Paper | MobileNets: Efficient CNNs for Mobile Vision Applications |
| License | Other |
| Downloads | 195,790 |
| Input Resolution | 192x192 |
What is mobilenet_v1_0.75_192?
MobileNet V1 0.75 192 is a lightweight convolutional neural network specifically designed for mobile and embedded vision applications. This variant operates at a 192x192 pixel resolution with a width multiplier of 0.75, offering an optimal balance between model size, computational efficiency, and accuracy.
Implementation Details
The model is implemented using PyTorch and supports the Transformers pipeline for easy integration. It's pre-trained on the ImageNet-1k dataset and includes 1001 output classes (1000 ImageNet classes plus a background class). The model employs depthwise separable convolutions to minimize computational overhead while maintaining good performance.
- Optimized for mobile deployment with reduced parameters
- Supports 192x192 input resolution
- Implements efficient depthwise separable convolutions
- Pre-trained on ImageNet-1k dataset
Core Capabilities
- Image Classification
- Mobile Vision Tasks
- Efficient Inference
- Low-latency Prediction
- Resource-constrained Deployment
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture that specifically targets mobile devices, offering a carefully balanced trade-off between model size, speed, and accuracy. The 0.75 width multiplier and 192x192 resolution provide a sweet spot for many mobile applications.
Q: What are the recommended use cases?
The model is ideal for mobile and embedded vision applications requiring real-time image classification. It's particularly suitable for scenarios where computational resources are limited, such as mobile apps, edge devices, and IoT applications requiring visual recognition capabilities.
