vit_base_patch16_224.mae
| Property | Value |
|---|---|
| Parameter Count | 85.8M |
| Model Type | Vision Transformer (ViT) |
| Input Size | 224 x 224 pixels |
| GMACs | 17.6 |
| Activations | 23.9M |
| Training Dataset | ImageNet-1k |
What is vit_base_patch16_224.mae?
vit_base_patch16_224.mae is a Vision Transformer model that has been pretrained using the Masked Autoencoder (MAE) self-supervised learning approach. This model divides input images into 16x16 pixel patches and processes them through a transformer architecture to learn robust visual representations without requiring manual labels.
Implementation Details
The model implements the architecture described in the "Masked Autoencoders Are Scalable Vision Learners" paper. It features a base-sized ViT architecture that processes 224x224 pixel images by first splitting them into 16x16 patches. The model can be used both for image classification and feature extraction tasks.
- Base architecture with 85.8M parameters
- 16x16 pixel patch size for image tokenization
- Pretrained using self-supervised MAE approach
- Supports both classification and embedding extraction
Core Capabilities
- Image classification with ImageNet-1k categories
- Feature extraction for downstream tasks
- Efficient self-supervised learning
- Robust visual representation learning
Frequently Asked Questions
Q: What makes this model unique?
This model combines the Vision Transformer architecture with MAE pretraining, allowing it to learn powerful visual representations without requiring labeled data. The self-supervised approach makes it particularly effective for transfer learning and feature extraction tasks.
Q: What are the recommended use cases?
The model is well-suited for image classification tasks, feature extraction for downstream applications, and transfer learning scenarios where robust visual representations are needed. It's particularly effective when working with limited labeled data due to its self-supervised pretraining.
