Vision Transformer Large Patch14 CLIP
| Property | Value |
|---|---|
| Parameter Count | 304.2M |
| License | Apache-2.0 |
| Image Size | 224 x 224 |
| GMACs | 77.8 |
| Activations | 57.1M |
| Primary Paper | CLIP Paper |
What is vit_large_patch14_clip_224.openai_ft_in12k_in1k?
This is a sophisticated Vision Transformer (ViT) model that leverages CLIP pretraining on WIT-400M image-text pairs, followed by fine-tuning on ImageNet-12k and ImageNet-1k datasets. It represents a powerful advancement in visual understanding, combining the benefits of transformer architecture with extensive pretraining on diverse datasets.
Implementation Details
The model employs a large Vision Transformer architecture with 14x14 pixel patches and operates on 224x224 pixel images. It features 304.2M parameters and efficiently processes visual information through self-attention mechanisms.
- Leverages CLIP pretraining methodology
- Multi-stage fine-tuning process
- Optimized for both classification and feature extraction
- Supports both direct classification and embedding generation
Core Capabilities
- High-accuracy image classification
- Robust feature extraction for downstream tasks
- Efficient processing of 224x224 images
- Flexible usage as both classifier and feature extractor
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its comprehensive training pipeline: CLIP pretraining on WIT-400M, followed by sequential fine-tuning on ImageNet-12k and ImageNet-1k. This creates a robust visual understanding system that combines zero-shot capabilities with traditional supervised learning benefits.
Q: What are the recommended use cases?
The model excels in image classification tasks and can be effectively used for feature extraction in various computer vision applications. It's particularly suitable for applications requiring high-quality image understanding and representation learning.
