CaiT M36 384 Image Transformer
| Property | Value |
|---|---|
| Parameter Count | 271.2M |
| License | Apache-2.0 |
| Framework | PyTorch (timm) |
| Input Size | 384 x 384 |
| Paper | Going deeper with Image Transformers |
What is cait_m36_384.fb_dist_in1k?
The CaiT M36 384 is a Class-Attention in Image Transformers model, representing a significant advancement in vision transformer architecture. Developed by Facebook Research, this model features 271.2M parameters and is specifically designed to process high-resolution images at 384x384 pixels. It was trained on ImageNet-1k with knowledge distillation techniques to enhance performance.
Implementation Details
This model operates with 173.1 GMACs and maintains 734.8M activations during processing. It's implemented in the timm library, providing seamless integration for both classification and feature extraction tasks. The architecture employs a sophisticated class-attention mechanism that allows for deeper network architectures while maintaining computational efficiency.
- Optimized for 384x384 input resolution
- Implements class-attention mechanism for improved feature learning
- Supports both classification and embedding extraction
- Includes distillation-based training improvements
Core Capabilities
- High-resolution image classification
- Feature extraction for downstream tasks
- Efficient processing of large-scale datasets
- Support for both standard classification and embedding generation
Frequently Asked Questions
Q: What makes this model unique?
The CaiT architecture introduces a novel class-attention mechanism that enables deeper transformer architectures for vision tasks, setting it apart from traditional vision transformers. The model's distillation training and optimization for 384x384 resolution images make it particularly effective for high-detail image analysis.
Q: What are the recommended use cases?
This model excels in scenarios requiring high-resolution image classification, feature extraction for transfer learning, and applications needing robust visual understanding. It's particularly well-suited for tasks where detail preservation is crucial, such as medical imaging, satellite imagery analysis, or fine-grained object recognition.
