DeiT-III Small Patch16 384
| Property | Value |
|---|---|
| Parameter Count | 22.2M |
| GMACs | 15.5 |
| Image Size | 384 x 384 |
| Paper | DeiT III: Revenge of the ViT |
| Source | Facebook Research DeiT |
What is deit3_small_patch16_384.fb_in22k_ft_in1k?
This is a specialized Vision Transformer (ViT) model from the DeiT-III family, designed for high-performance image classification tasks. It represents a small variant of the architecture that has been pretrained on ImageNet-22k and fine-tuned on ImageNet-1k, optimized for processing 384x384 pixel images.
Implementation Details
The model employs a patch-based approach, dividing input images into 16x16 pixel patches. With 22.2M parameters and 15.5 GMACs, it offers an efficient balance between computational cost and performance. The architecture includes attention mechanisms and transformers specifically adapted for vision tasks.
- Pretrained on ImageNet-22k for robust feature learning
- Fine-tuned on ImageNet-1k for specific classification tasks
- Optimized for 384x384 resolution inputs
- Features 50.8M activations during processing
Core Capabilities
- Image Classification with state-of-the-art accuracy
- Feature extraction for downstream tasks
- Efficient processing of high-resolution images
- Adaptable for transfer learning applications
Frequently Asked Questions
Q: What makes this model unique?
This model stands out through its efficient architecture that combines the benefits of Vision Transformers with practical deployment considerations. The two-stage training (pretraining on ImageNet-22k followed by fine-tuning on ImageNet-1k) provides robust feature learning while maintaining reasonable computational requirements.
Q: What are the recommended use cases?
The model is particularly well-suited for image classification tasks requiring high accuracy on standard resolution images. It can be effectively used for feature extraction in transfer learning scenarios or as a backbone for more complex computer vision tasks.
