Res2Next50.in1k
| Property | Value |
|---|---|
| Parameters | 24.7M |
| GMACs | 4.2 |
| Activations | 13.7M |
| Input Size | 224 x 224 |
| Paper | Res2Net: A New Multi-scale Backbone Architecture |
What is res2next50.in1k?
Res2Next50.in1k is an advanced implementation of the Res2Net architecture, specifically designed for multi-scale feature representation in computer vision tasks. This model has been pre-trained on the ImageNet-1k dataset and serves as both a standalone image classifier and a powerful feature backbone for various downstream tasks.
Implementation Details
The model implements a sophisticated multi-scale processing approach within a single neural network, utilizing 24.7M parameters and requiring 4.2 GMACs for inference. It processes images at 224x224 resolution and generates feature maps at multiple scales, making it particularly effective for tasks requiring multi-scale understanding.
- Hierarchical feature extraction with 5 different scales
- Efficient parameter utilization with 24.7M parameters
- Flexible architecture supporting both classification and feature extraction
- Pre-trained on ImageNet-1k for robust feature learning
Core Capabilities
- Image Classification: Direct classification with 1000-class ImageNet categories
- Feature Map Extraction: Generates multi-scale feature maps (from 112x112 to 7x7)
- Image Embeddings: Produces 2048-dimensional feature vectors
- Backbone Architecture: Can be used as a feature extractor for downstream tasks
Frequently Asked Questions
Q: What makes this model unique?
The Res2Next50 model stands out for its multi-scale processing capability within a single network layer, allowing it to capture both fine and coarse features efficiently. Its architecture builds upon the successful ResNet design while introducing novel scale-wise feature extraction.
Q: What are the recommended use cases?
This model is particularly well-suited for tasks requiring multi-scale feature understanding, including image classification, object detection, and semantic segmentation. It's especially effective when deployed as a backbone network in more complex computer vision architectures.
