PiT-B 224 ImageNet-1K Model
| Property | Value |
|---|---|
| Parameter Count | 73.8M |
| GMACs | 12.4 |
| Image Size | 224x224 |
| License | Apache-2.0 |
| Paper | Rethinking Spatial Dimensions of Vision Transformers |
What is pit_b_224.in1k?
PiT-B is a sophisticated Pooling-based Vision Transformer model that reimagines the spatial dimensions approach in vision transformers. Developed by researchers at NAVER AI and published in ICCV 2021, this model represents a significant advancement in the field of computer vision, particularly in image classification tasks.
Implementation Details
The model employs a hybrid architecture that combines the strengths of convolutional neural networks with transformer-based approaches. With 73.8M parameters and 32.9M activations, it achieves efficient processing of 224x224 images while maintaining strong performance on the ImageNet-1K dataset.
- Optimized parameter efficiency with 73.8M parameters
- Designed for 224x224 resolution images
- Features pooling-based architecture for improved spatial processing
- Implements efficient feature extraction capabilities
Core Capabilities
- Image Classification with high accuracy on ImageNet-1K
- Feature Map Extraction with multiple resolution outputs
- Image Embedding Generation for downstream tasks
- Flexible integration through the timm library
Frequently Asked Questions
Q: What makes this model unique?
PiT-B stands out for its innovative approach to handling spatial dimensions in vision transformers, combining pooling operations with transformer architecture to achieve better efficiency and performance.
Q: What are the recommended use cases?
This model is particularly well-suited for image classification tasks, feature extraction, and generating image embeddings for transfer learning applications. It's ideal for scenarios requiring high-quality image analysis at 224x224 resolution.
