res2net101_26w_4s.in1k
| Property | Value |
|---|---|
| Parameter Count | 45.3M |
| Model Type | Image Classification |
| Architecture | Res2Net |
| Paper | Res2Net: A New Multi-scale Backbone Architecture |
| License | Unknown |
What is res2net101_26w_4s.in1k?
res2net101_26w_4s.in1k is an advanced image classification model that implements the innovative Res2Net architecture. With 45.3M parameters and trained on the ImageNet-1k dataset, it represents a significant evolution in multi-scale feature extraction for computer vision tasks. The model processes images of size 224x224 and employs a sophisticated backbone that enables hierarchical residual-like connections within each network layer.
Implementation Details
The model utilizes a multi-scale feature extraction approach with 8.1 GMACs computational complexity and 18.4M activations. It's implemented using PyTorch through the timm library and supports both F32 tensor operations.
- Hierarchical residual-like feature extraction
- Multi-scale processing capability
- Optimized for 224x224 input images
- Supports feature map extraction and image embeddings
Core Capabilities
- Image classification with state-of-the-art accuracy
- Feature backbone for transfer learning
- Multi-scale feature extraction
- Flexible deployment options through timm library
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its multi-scale processing capability within each network layer, allowing it to capture visual patterns at various granularities simultaneously. This architecture provides superior feature representation compared to traditional ResNet models.
Q: What are the recommended use cases?
This model excels in image classification tasks, particularly those requiring fine-grained feature extraction. It's also valuable as a backbone for transfer learning in computer vision applications like object detection, semantic segmentation, and instance segmentation.
