H

Hiera Base 224

Developed by namangarg110
Hiera is an efficient hierarchical Transformer architecture that optimizes spatial bias characteristics through MAE training, significantly improving parameter utilization efficiency
Downloads 48
Release Time : 2/28/2024

Model Overview

Hiera is a hierarchical Transformer model designed for visual tasks, achieving high-precision image processing while maintaining efficient operation through multi-scale feature processing and MAE pretraining strategies

Model Features

Hierarchical Architecture Design
Adopts a multi-scale processing approach with shallow high-resolution and deep low-resolution layers, mimicking the feature extraction pattern of traditional CNNs
MAE Pretraining Optimization
Utilizes Masked Autoencoder (MAE) training strategy to endow the model with spatial bias characteristics, improving parameter utilization efficiency
Efficient Operation
Compared to traditional hierarchical Transformer models (e.g., ConvNeXT/Swin), it significantly improves operational speed while maintaining accuracy

Model Capabilities

Image Feature Extraction
Multi-scale Visual Representation Learning
Efficient Image Processing

Use Cases

Computer Vision
Image Classification
Suitable for standard image classification tasks such as ImageNet
Visual Feature Extraction
Can serve as a feature extractor for downstream visual tasks (e.g., object detection, segmentation)
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
© 2025AIbase