ViT-Large-Patch16-384
| Property | Value |
|---|---|
| Model Type | Vision Transformer (ViT) |
| Training Data | ImageNet-21k + ImageNet 2012 |
| Input Resolution | 384x384 pixels |
| Patch Size | 16x16 pixels |
| Author |
What is vit-large-patch16-384?
The vit-large-patch16-384 is a large-scale Vision Transformer model that represents a significant advancement in computer vision. Initially pre-trained on ImageNet-21k with 14 million images across 21,843 classes at 224x224 resolution, then fine-tuned on ImageNet 2012 at 384x384 resolution. This model transforms images into sequences of 16x16 pixel patches and processes them using transformer architecture, similar to how BERT processes text.
Implementation Details
The model employs a sophisticated preprocessing pipeline where images are resized to 384x384 pixels and normalized with mean and standard deviation of 0.5 across RGB channels. It uses a [CLS] token for classification tasks and incorporates absolute position embeddings before feeding the sequence through the transformer encoder.
- Processes images as sequences of 16x16 pixel patches
- Utilizes transformer encoder architecture
- Includes specialized [CLS] token for classification
- Trained on TPUv3 hardware with 8 cores
- Uses batch size of 4096 with 10k steps learning rate warmup
Core Capabilities
- Image classification across 1,000 ImageNet classes
- Feature extraction for downstream tasks
- High-resolution image processing (384x384)
- Transfer learning applications
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its large-scale training on ImageNet-21k and subsequent fine-tuning on ImageNet 2012, combined with its ability to process higher resolution images (384x384) compared to standard models. The patch-based approach and transformer architecture provide excellent performance on image classification tasks.
Q: What are the recommended use cases?
The model is primarily designed for image classification tasks but can be effectively used for feature extraction in transfer learning scenarios. It's particularly well-suited for applications requiring high-resolution image understanding and those that can benefit from the rich feature representations learned from the large-scale pre-training.
