tf_mobilenetv3_small_minimal_100.in1k
| Property | Value |
|---|---|
| Parameter Count | 2.06M |
| Model Type | Image Classification |
| License | Apache-2.0 |
| Paper | Searching for MobileNetV3 |
| Image Size | 224 x 224 |
| GMACs | 0.1 |
What is tf_mobilenetv3_small_minimal_100.in1k?
This is a lightweight MobileNetV3 variant specifically designed for efficient image classification. Originally developed in TensorFlow by the paper authors and later ported to PyTorch by Ross Wightman, this model represents a minimal implementation of the MobileNetV3-Small architecture, optimized for reduced computational requirements while maintaining reasonable accuracy on ImageNet-1k.
Implementation Details
The model features a compact architecture with just 2.06M parameters and requires only 0.1 GMACs for inference. It processes images at 224x224 resolution and utilizes an efficient design that makes it particularly suitable for mobile and edge devices. The implementation includes support for both classification and feature extraction tasks, with the ability to output intermediate feature maps at various network depths.
- Efficient architecture with minimal computational overhead
- Supports both classification and feature extraction modes
- Pre-trained on ImageNet-1k dataset
- Flexible feature map extraction capabilities
Core Capabilities
- Image classification with 1000 ImageNet classes
- Feature map extraction at multiple scales
- Image embedding generation
- Transfer learning compatibility
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its minimal design approach while maintaining the core benefits of MobileNetV3 architecture. Its small parameter count (2.06M) and low computational requirements make it ideal for resource-constrained environments.
Q: What are the recommended use cases?
The model is best suited for mobile applications, edge devices, and scenarios where computational resources are limited but image classification capabilities are needed. It's particularly effective for real-time classification tasks that don't require extremely high accuracy but demand efficiency.
