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 uses a multimodal autoregressive pre-training approach. This model represents a significant advancement in computer vision, featuring a 224x224 pixel input resolution and patch size of 14x14. It achieves remarkable performance across various vision tasks, with 87.5% accuracy on ImageNet-1k classification.
Implementation Details
The model utilizes a transformer-based architecture optimized for image feature extraction. It's implemented with cross-platform support, available in PyTorch, JAX, and MLX frameworks. The model processes images using a patch-based approach and can be easily integrated into existing pipelines using the Hugging Face transformers library.
- 681M trainable parameters
- 14x14 pixel patch encoding
- 224x224 input resolution
- Supports multiple deep learning frameworks
Core Capabilities
- Image Classification (87.5% ImageNet accuracy)
- Feature Extraction for downstream tasks
- Strong performance on specialized datasets (96.3% on Food101, 96.6% on Oxford-Pets)
- Multimodal understanding capabilities
- Outperforms CLIP and SigLIP on various benchmarks
Frequently Asked Questions
Q: What makes this model unique?
The model's multimodal autoregressive pre-training approach sets it apart, enabling superior performance across various vision tasks while maintaining efficiency with 681M parameters. It particularly excels in transfer learning scenarios and specialized domain applications.
Q: What are the recommended use cases?
This model is ideal for image classification, feature extraction, and transfer learning tasks. It shows exceptional performance on specialized datasets like medical imaging (93.3% on Camelyon17) and fine-grained classification tasks like Stanford Cars (96.4%).
