| Property | Value |
|---|---|
| Parameters | 11.0M |
| GMACs | 2.0 |
| Image Size | 224 x 224 |
| License | Apache-2.0 |
| Paper | Co-Scale Conv-Attentional Image Transformers |
What is coat_lite_mini.in1k?
coat_lite_mini.in1k is a lightweight implementation of the Co-Scale Conv-Attentional Transformer (CoaT) architecture, specifically designed for efficient image classification tasks. This model represents a innovative approach to combining convolutional neural networks with transformer architectures, optimized for both performance and computational efficiency.
Implementation Details
The model features a hybrid architecture that leverages both convolutional and attention mechanisms. With 11.0M parameters and 2.0 GMACs, it strikes an excellent balance between model size and computational requirements. The model processes images at 224x224 resolution and maintains 12.2M activations during operation.
- Efficient hybrid architecture combining CNN and transformer components
- Optimized for ImageNet-1k classification tasks
- Supports both classification and feature extraction workflows
Core Capabilities
- Image Classification: Provides robust classification performance on ImageNet-1k dataset
- Feature Extraction: Can be used as a backbone for various computer vision tasks
- Embedding Generation: Supports extraction of image embeddings for downstream tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's co-scale attention mechanism allows it to process visual information at multiple scales simultaneously, making it particularly effective for capturing both local and global image features while maintaining computational efficiency.
Q: What are the recommended use cases?
This model is ideal for image classification tasks, particularly when deployment efficiency is a concern. It's also suitable for feature extraction in transfer learning scenarios and can be effectively used as a backbone for various computer vision applications.