AIMv2-Huge-Patch14-224
| Property | Value |
|---|---|
| Parameter Count | 681M parameters |
| License | Apple ASCL |
| Paper | arXiv:2411.14402 |
| Architecture | Vision Transformer (ViT) |
| Input Resolution | 224x224 pixels |
What is aimv2-huge-patch14-224?
AIMv2-huge-patch14-224 is a state-of-the-art vision model developed by Apple, pre-trained with a multimodal autoregressive objective. This model represents a significant advancement in computer vision, featuring impressive performance across various image recognition tasks. With 681M parameters, it achieves 87.5% accuracy on ImageNet-1k and demonstrates exceptional capabilities across multiple domains.
Implementation Details
The model supports both PyTorch and JAX frameworks, making it versatile for different development environments. It processes images with 14x14 pixel patches and operates at 224x224 resolution, utilizing a transformer-based architecture for feature extraction.
- Supports multiple tensor frameworks including PyTorch, JAX, and MLX
- Implements patch-based image processing (14x14 patches)
- Features a multimodal autoregressive pre-training approach
Core Capabilities
- ImageNet-1k Classification: 87.5% accuracy
- CIFAR-10 Classification: 99.3% accuracy
- Food101 Classification: 96.3% accuracy
- Oxford-Pets Classification: 96.6% accuracy
- EuroSAT Classification: 98.5% accuracy
Frequently Asked Questions
Q: What makes this model unique?
AIMv2 outperforms both OAI CLIP and SigLIP on most multimodal understanding benchmarks, while also showing superior performance compared to DINOv2 on open-vocabulary object detection and referring expression comprehension.
Q: What are the recommended use cases?
The model excels in image feature extraction, classification tasks, and multimodal understanding. It's particularly effective for high-precision image classification tasks across various domains including natural images, medical imaging (Camelyon17: 93.3%), and satellite imagery (EuroSAT: 98.5%).
