ResNeSt101e ImageNet Model
| Property | Value |
|---|---|
| Parameter Count | 48.4M |
| Model Type | Image Classification |
| Architecture | ResNeSt with Split-Attention |
| License | Apache-2.0 |
| Paper | ResNeSt: Split-Attention Networks |
What is resnest101e.in1k?
The resnest101e.in1k is a sophisticated image classification model based on the ResNeSt architecture, which enhances the traditional ResNet design with split-attention mechanisms. Trained on the ImageNet-1k dataset, this model offers robust feature extraction capabilities with 48.4M parameters and is optimized for 256x256 image inputs.
Implementation Details
This implementation features a highly efficient architecture with 13.4 GMACs computational complexity and 28.7M activations. It's built using the PyTorch framework through the TIMM library, offering seamless integration for both inference and feature extraction tasks.
- Split-attention mechanism for enhanced feature representation
- Optimized for 256x256 input resolution
- Supports both classification and feature extraction modes
- Compatible with PyTorch and TIMM ecosystem
Core Capabilities
- Image classification with 1000 ImageNet classes
- Feature map extraction at multiple scales
- Image embedding generation
- Support for transfer learning applications
Frequently Asked Questions
Q: What makes this model unique?
The model's split-attention mechanism allows it to capture cross-feature interactions more effectively than traditional ResNet architectures, while maintaining computational efficiency. The 'e' variant specifically offers enhanced performance through architectural optimizations.
Q: What are the recommended use cases?
This model is ideal for high-accuracy image classification tasks, transfer learning applications, and as a backbone for more complex computer vision tasks like object detection or segmentation. It's particularly suitable when working with high-resolution images requiring detailed feature extraction.
