ResNeSt14d GluonCV Model
| Property | Value |
|---|---|
| Parameters | 10.6M |
| GMACs | 2.8 |
| Image Size | 224 x 224 |
| Paper | ResNeSt: Split-Attention Networks |
| Framework | GluonCV |
What is resnest14d.gluon_in1k?
ResNeSt14d is a sophisticated implementation of the ResNeSt architecture, which enhances the traditional ResNet design with split-attention mechanisms. This particular model represents a lightweight variant trained on the ImageNet-1k dataset, offering an excellent balance between computational efficiency and performance.
Implementation Details
The model features a split-attention network architecture that builds upon ResNet's foundation, incorporating innovative attention mechanisms to improve feature representation. With 10.6M parameters and 2.8 GMACs, it's designed for efficient inference while maintaining strong performance.
- Built with split-attention blocks for enhanced feature learning
- Optimized activation size of 7.3M
- Supports 224x224 input images
- Includes feature extraction capabilities with multiple output scales
Core Capabilities
- Image classification with 1000 ImageNet classes
- Feature map extraction at multiple scales
- Image embedding generation
- Support for both inference and transfer learning
Frequently Asked Questions
Q: What makes this model unique?
The ResNeSt14d combines split-attention mechanisms with a lightweight architecture, making it particularly efficient for real-world applications while maintaining competitive accuracy. Its architecture allows for better feature representation compared to traditional ResNet models of similar size.
Q: What are the recommended use cases?
This model is ideal for image classification tasks, feature extraction, and as a backbone for transfer learning in computer vision applications where computational efficiency is important. It's particularly well-suited for scenarios requiring a good balance between model size and performance.
