| Property | Value |
|---|---|
| Parameter Count | 196.7M |
| GMACs | 47.8 |
| Image Size | 256x256 |
| Paper | Swin Transformer V2: Scaling Up Capacity and Resolution |
| Pre-training | ImageNet-22k |
| Fine-tuning | ImageNet-1k |
What is swinv2_large_window12to16_192to256.ms_in22k_ft_in1k?
This is an advanced implementation of the Swin Transformer V2 architecture, designed for high-performance image classification and feature extraction. The model represents a significant evolution in vision transformer technology, incorporating adaptive window sizes (12 to 16) and supporting variable image resolutions (192 to 256 pixels).
Implementation Details
The model features a sophisticated architecture with 196.7M parameters and requires 47.8 GMACs for inference. It utilizes a hierarchical design with shifted windows, making it particularly efficient for processing high-resolution images while maintaining computational efficiency.
- Pre-trained on ImageNet-22k for robust feature learning
- Fine-tuned on ImageNet-1k for specific classification tasks
- Supports variable window sizes from 12 to 16
- Optimized for image resolutions between 192x192 and 256x256
Core Capabilities
- Image Classification with state-of-the-art accuracy
- Feature Map Extraction at multiple scales
- Image Embedding generation
- Flexible input resolution handling
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its adaptive window sizing mechanism and its dual-stage training approach (pre-training on ImageNet-22k and fine-tuning on ImageNet-1k). The large parameter count of 196.7M enables it to capture complex image features effectively.
Q: What are the recommended use cases?
The model is particularly well-suited for high-precision image classification tasks, feature extraction for downstream tasks, and scenarios requiring robust visual understanding. Its variable resolution support makes it versatile for different input sizes.