EVA02 Large Patch14 448
| Property | Value |
|---|---|
| Parameter Count | 305.1M |
| Model Type | Vision Transformer |
| License | MIT |
| Image Size | 448 x 448 |
| Top-1 Accuracy | 90.054% |
| Paper | EVA-02: A Visual Representation for Neon Genesis |
What is eva02_large_patch14_448.mim_m38m_ft_in22k_in1k?
This is an advanced vision transformer model from the EVA02 family, designed for high-performance image classification tasks. Pre-trained on the Merged-38M dataset (which includes ImageNet-22k, CC12M, CC3M, COCO, ADE20K, Object365, and OpenImages), it implements masked image modeling using EVA-CLIP as a teacher model.
Implementation Details
The model represents a significant advancement in vision transformer architecture, incorporating several key innovations:
- Mean pooling for improved feature aggregation
- SwiGLU activation functions for enhanced learning capability
- Rotary Position Embeddings (ROPE) for better spatial understanding
- Additional Layer Normalization in MLP blocks
- Patch size of 14x14 pixels
- 448x448 input resolution
Core Capabilities
- State-of-the-art image classification performance (90.054% top-1 accuracy)
- Robust feature extraction for downstream tasks
- Efficient processing of high-resolution images
- Strong transfer learning capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its comprehensive pre-training on the Merged-38M dataset and its advanced architectural features like ROPE and SwiGLU. It achieves exceptional accuracy while maintaining reasonable computational requirements for its size class.
Q: What are the recommended use cases?
The model is ideal for high-precision image classification tasks, feature extraction for downstream applications, and transfer learning scenarios where high accuracy is crucial. It's particularly well-suited for applications requiring detailed image analysis at higher resolutions.
