TinyNet-A Image Classification Model
| Property | Value |
|---|---|
| Parameter Count | 6.24M |
| Model Type | Image Classification |
| Input Size | 192x192 |
| License | Apache-2.0 |
| Paper | Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets |
What is tinynet_a.in1k?
TinyNet-A is a compact and efficient image classification model that represents a breakthrough in neural network optimization. Developed through the innovative "Model Rubik's Cube" approach, it achieves an optimal balance between model size, computational complexity, and accuracy. With just 6.24M parameters and designed for 192x192 resolution images, it demonstrates how carefully tuned architecture can deliver efficient performance.
Implementation Details
The model utilizes a sophisticated architecture with 0.3 GMACs and 5.4M activations. It's implemented in PyTorch through the TIMM library, offering straightforward integration for both inference and feature extraction tasks. The model supports various operational modes, including classification, feature map extraction, and image embedding generation.
- Optimized for 192x192 input resolution
- Features a hierarchical feature extraction pipeline
- Supports both classification and backbone functionality
- Trained on the ImageNet-1k dataset
Core Capabilities
- Direct image classification with 1000-class ImageNet categories
- Feature map extraction at multiple scales
- Image embedding generation for transfer learning
- Efficient inference with F32 tensor support
Frequently Asked Questions
Q: What makes this model unique?
TinyNet-A stands out for its innovative architecture optimization approach using the "Model Rubik's Cube" methodology, which carefully balances resolution, depth, and width to achieve efficient performance with minimal parameters.
Q: What are the recommended use cases?
The model is ideal for resource-constrained applications requiring image classification, feature extraction, or as a backbone for transfer learning tasks. It's particularly suitable for mobile and edge devices where model size and computational efficiency are crucial.
