Hiera Base 224
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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
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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)
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