nest_base_jx.goog_in1k
| Property | Value |
|---|---|
| Parameter Count | 67.7M |
| Model Type | Image Classification |
| License | Apache-2.0 |
| Paper | View Paper |
| Dataset | ImageNet-1k |
What is nest_base_jx.goog_in1k?
nest_base_jx.goog_in1k is a sophisticated image classification model based on the Nested Hierarchical Transformer architecture. Originally developed by Google Research and trained in JAX, this model has been successfully ported to PyTorch. It represents a significant advancement in efficient visual understanding, combining the power of transformers with a nested hierarchical structure.
Implementation Details
The model features a complex architecture with 67.7M parameters and requires 18.0 GMACs for inference. It processes images at 224x224 resolution and generates 53.4M activations. The implementation supports various usage modes including classification, feature map extraction, and image embedding generation.
- Hierarchical transformer architecture for efficient processing
- Supports both classification and feature extraction
- Includes pretrained weights on ImageNet-1k
- Provides flexible API for different use cases
Core Capabilities
- Image classification with high accuracy
- Feature map extraction at multiple scales
- Generation of image embeddings
- Support for transfer learning applications
Frequently Asked Questions
Q: What makes this model unique?
The model's nested hierarchical structure sets it apart, offering a balance between accuracy and computational efficiency. It's particularly notable for its interpretable visual understanding capabilities and data-efficient training approach.
Q: What are the recommended use cases?
This model is ideal for image classification tasks, feature extraction, and as a backbone for transfer learning in computer vision applications. It's particularly suitable for scenarios requiring both accuracy and interpretability.
