Vision Transformer (ViT)

By ModelHub Team • 12345 downloads

67.2%

Accuracy

512

Layers

6.2GB

Model Size

720ms

Inference

The Vision Transformer is a state-of-the-art model that applies the concept of transformers to image recognition tasks. It divides images into patches and processes them using multi-head attention mechanisms, achieving excellent performance on benchmark datasets like ImageNet.

High Accuracy

Achieves top performance on benchmark datasets.

Efficient

Optimized for performance with memory efficiency.

Customizable

Supports model fine-tuning for specific tasks.

Easy to Use

Simple API and intuitive model interface.

Quick Usage

from modelhub import ViT

# Initialize model
model = ViT(weights="ImageNet")

# Make prediction
result = model.predict(img_path="path/to/image.jpg")

# Get confidence
print(result.confidence)  # 0.94