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 a state-of-the-art vision model developed by Apple that leverages multimodal autoregressive pre-training. This model represents a significant advancement in computer vision, featuring 681M parameters and achieving an impressive 87.5% accuracy on ImageNet-1k.
Implementation Details
The model utilizes a transformer-based architecture with patch size 14 and input resolution of 224x224. It's implemented with support for multiple frameworks including PyTorch, JAX, and MLX, making it versatile for different development environments.
- Extensive dataset performance validation across multiple domains
- Strong performance on specialized datasets (96.3% on Food101, 96.6% on Oxford-Pets)
- Supports both feature extraction and classification tasks
Core Capabilities
- Image Feature Extraction
- Classification across diverse domains
- High accuracy on standard benchmarks (99.3% on CIFAR10)
- Robust performance on medical imaging (93.3% on Camelyon17)
Frequently Asked Questions
Q: What makes this model unique?
AIMv2 stands out for its multimodal autoregressive pre-training approach, outperforming CLIP and SigLIP on various multimodal understanding benchmarks. It's particularly notable for its strong performance without fine-tuning.
Q: What are the recommended use cases?
The model excels in image classification tasks across various domains including natural images, medical imaging, satellite imagery, and fine-grained classification tasks. It's particularly suitable for applications requiring high-quality feature extraction.
