CAFormer B36 Vision Model
| Property | Value |
|---|---|
| Parameter Count | 98.8M |
| License | Apache 2.0 |
| Paper | Metaformer Baselines for Vision |
| Image Size | 224 x 224 |
| GMACs | 23.2 |
What is caformer_b36.sail_in22k_ft_in1k?
The CAFormer B36 is a sophisticated MetaFormer architecture designed for computer vision tasks. Initially pretrained on the extensive ImageNet-22k dataset and subsequently fine-tuned on ImageNet-1k, this model represents a state-of-the-art approach to image classification and feature extraction. With 98.8M parameters, it strikes a balance between model complexity and performance.
Implementation Details
This model leverages the MetaFormer architecture, incorporating advanced features for efficient image processing. It operates on 224x224 pixel images and uses 23.2 GMACs (Giga Multiply-Accumulate Operations), demonstrating its computational efficiency despite its substantial parameter count.
- Flexible feature extraction capabilities with multiple output formats
- Support for both classification and embedding generation
- Optimized activation size of 67.3M
- Compatible with the timm library for easy integration
Core Capabilities
- Image Classification with high accuracy on ImageNet-1k
- Feature map extraction at multiple scales
- Generation of image embeddings for downstream tasks
- Support for both inference and feature extraction workflows
Frequently Asked Questions
Q: What makes this model unique?
The CAFormer B36 stands out due to its MetaFormer architecture and dual-stage training approach (ImageNet-22k pretraining followed by ImageNet-1k fine-tuning), making it particularly robust for various vision tasks.
Q: What are the recommended use cases?
This model excels in image classification tasks, feature extraction for downstream applications, and generating image embeddings for transfer learning scenarios. It's particularly suitable for applications requiring high-quality visual feature representation.
