VOLO D1-224 Vision Model
| Property | Value |
|---|---|
| Parameter Count | 26.6M |
| Model Type | Image Classification |
| Architecture | Vision Outlooker (VOLO) |
| License | Apache-2.0 |
| Paper | VOLO: Vision Outlooker for Visual Recognition |
What is volo_d1_224.sail_in1k?
VOLO D1-224 is an advanced vision model that implements the Vision Outlooker architecture for image classification tasks. Developed by researchers from SAIL, this model represents a significant advancement in visual recognition systems, utilizing a 224x224 input resolution and incorporating token labelling techniques.
Implementation Details
The model features 26.6M parameters and operates with 6.9 GMACs (Giga Multiply-Accumulate Operations). It processes images through a sophisticated architecture that includes 24.4M activations, making it efficient for both classification and feature extraction tasks.
- Optimized for 224x224 image inputs
- Supports both classification and embedding generation
- Implements token labelling for enhanced performance
- Uses F32 tensor type for calculations
Core Capabilities
- Image Classification with ImageNet-1k classes
- Feature backbone functionality for transfer learning
- Embedding generation for downstream tasks
- Efficient visual recognition with state-of-the-art accuracy
Frequently Asked Questions
Q: What makes this model unique?
VOLO D1-224 stands out for its implementation of the Vision Outlooker architecture, which provides superior visual recognition capabilities while maintaining computational efficiency. The incorporation of token labelling and its balanced parameter count makes it particularly effective for practical applications.
Q: What are the recommended use cases?
The model is ideal for image classification tasks, feature extraction, and as a backbone for transfer learning applications. It's particularly well-suited for scenarios requiring 224x224 image processing with high accuracy requirements.
