Vision Transformer Small (ViT-Small)
| Property | Value |
|---|---|
| Parameter Count | 22.1M |
| Model Type | Vision Transformer |
| License | Apache-2.0 |
| Image Size | 224x224 |
| GMACs | 4.3 |
| Activations | 8.2M |
What is vit_small_patch16_224.augreg_in21k_ft_in1k?
This is a Vision Transformer (ViT) model specifically designed for image classification tasks. Initially trained on the extensive ImageNet-21k dataset and then fine-tuned on ImageNet-1k, this model implements advanced augmentation and regularization techniques to enhance performance. Developed by Google Research and ported to PyTorch by Ross Wightman, it represents a modern approach to computer vision using transformer architecture.
Implementation Details
The model employs a patch-based approach where images are divided into 16x16 patches and processed using transformer architecture. With 22.1M parameters, it strikes a balance between model capacity and computational efficiency. The model operates on 224x224 pixel images and utilizes the F32 tensor type for computations.
- Patch Size: 16x16 pixels
- Pretrained on ImageNet-21k with fine-tuning on ImageNet-1k
- Incorporates additional augmentation and regularization techniques
- Optimized for both classification and feature extraction tasks
Core Capabilities
- Image Classification: Direct classification with softmax probabilities
- Feature Extraction: Can output embeddings for downstream tasks
- Flexible Integration: Easy to use with standard PyTorch workflows
- Transfer Learning: Suitable for fine-tuning on custom datasets
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its augmented training regime and dual-stage training process (ImageNet-21k pretraining followed by ImageNet-1k fine-tuning). The additional regularization techniques make it particularly robust for real-world applications.
Q: What are the recommended use cases?
The model is ideal for image classification tasks, feature extraction for downstream applications, and as a backbone for transfer learning. It's particularly well-suited for applications requiring a good balance between computational efficiency and accuracy.
