AIMv2-Huge-Patch14-224
| Property | Value |
|---|---|
| Parameter Count | 681M |
| License | Apple ASCL |
| Paper | arXiv:2411.14402 |
| Framework Support | PyTorch, JAX, MLX |
What is aimv2-huge-patch14-224?
AIMv2-huge-patch14-224 is an advanced vision model developed by Apple that utilizes a multimodal autoregressive pre-training approach. This model represents a significant advancement in computer vision, featuring impressive performance across various visual recognition tasks. With 681M parameters, it achieves 87.5% accuracy on ImageNet-1k and shows remarkable versatility across different domains.
Implementation Details
The model implements a transformer-based architecture with patch size 14 and input resolution of 224x224. It's designed for image feature extraction and supports multiple deep learning frameworks including PyTorch, JAX, and MLX.
- Pre-trained using multimodal autoregressive objectives
- Supports both feature extraction and classification tasks
- Implements patch-based image processing
- Available in F32 tensor format
Core Capabilities
- ImageNet-1k Classification: 87.5% accuracy
- CIFAR-10 Classification: 99.3% accuracy
- Food101 Dataset: 96.3% accuracy
- Oxford-Pets Classification: 96.6% accuracy
- Strong performance on transfer learning tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its multimodal autoregressive pre-training approach, outperforming CLIP and SigLIP on various multimodal understanding benchmarks while maintaining strong performance on traditional vision tasks.
Q: What are the recommended use cases?
The model excels in image feature extraction, classification tasks, and transfer learning applications. It's particularly well-suited for high-precision vision tasks requiring robust feature representation.
