Vision Transformer Base R50-S16-384
| Property | Value |
|---|---|
| Parameter Count | 99M |
| License | Apache 2.0 |
| Paper | An Image is Worth 16x16 Words |
| Image Size | 384 x 384 |
| GMACs | 61.3 |
What is vit_base_r50_s16_384.orig_in21k_ft_in1k?
This model represents a sophisticated hybrid architecture combining ResNet and Vision Transformer (ViT) technologies. Initially trained on the extensive ImageNet-21k dataset and subsequently fine-tuned on ImageNet-1k, it demonstrates exceptional capabilities in image classification tasks while leveraging the strengths of both convolutional and transformer-based approaches.
Implementation Details
The model utilizes a hybrid architecture with 99M parameters, processing images at 384x384 resolution. It features a ResNet-50 backbone combined with a Vision Transformer, using 16x16 patch sizes. The model requires 61.3 GMACs (Giga Multiply-Accumulate Operations) and maintains 81.8M activations during processing.
- Hybrid architecture combining ResNet-50 and Vision Transformer
- Pre-trained on ImageNet-21k for robust feature extraction
- Fine-tuned on ImageNet-1k for specific classification tasks
- Supports both classification and embedding extraction
Core Capabilities
- High-resolution image classification (384x384)
- Feature extraction and embedding generation
- Transfer learning applications
- State-of-the-art performance on computer vision tasks
Frequently Asked Questions
Q: What makes this model unique?
This model's hybrid architecture combines the local feature processing capabilities of ResNet-50 with the global attention mechanisms of Vision Transformers, offering a balanced approach to image understanding. The pre-training on ImageNet-21k followed by fine-tuning on ImageNet-1k provides robust performance across diverse scenarios.
Q: What are the recommended use cases?
The model excels in high-resolution image classification tasks, feature extraction for downstream tasks, and scenarios requiring robust visual understanding. It's particularly suitable for applications needing both local and global image feature analysis.
