res2net50_14w_8s.in1k
| Property | Value |
|---|---|
| Parameter Count | 25.1M |
| Model Type | Image Classification / Feature Backbone |
| Architecture | Res2Net (Multi-Scale ResNet) |
| Paper | Res2Net: A New Multi-scale Backbone Architecture |
| License | Unknown |
| Dataset | ImageNet-1k |
What is res2net50_14w_8s.in1k?
res2net50_14w_8s.in1k is an advanced implementation of the Res2Net architecture, specifically designed for image classification and feature extraction tasks. This model represents a significant evolution in multi-scale feature processing, with 25.1M parameters and optimized performance on the ImageNet-1k dataset. The model processes images at 224x224 resolution and requires 4.2 GMACs of computational power.
Implementation Details
The model implements a sophisticated multi-scale backbone architecture that enhances the traditional ResNet design. It features hierarchical residual-like connections within each single residual block, enabling multi-scale feature processing at a granular level. The model architecture efficiently handles 13.3M activations and provides flexible feature extraction capabilities at various scales.
- Hierarchical Residual Architecture
- Multi-scale Feature Processing
- 224x224 Input Resolution
- F32 Tensor Type Support
Core Capabilities
- Image Classification with high accuracy
- Feature Map Extraction at multiple scales
- Image Embedding Generation
- Flexible backbone for various computer vision tasks
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its multi-scale feature processing capability within single residual blocks, allowing for more refined feature hierarchies than traditional ResNet architectures. The '14w_8s' configuration indicates specific architectural choices that optimize the balance between performance and computational efficiency.
Q: What are the recommended use cases?
The model is particularly well-suited for image classification tasks, feature extraction, and as a backbone for more complex computer vision applications. It's ideal for scenarios requiring multi-scale feature analysis or transfer learning applications.
