Vit Base Patch16 224
The Vision Transformer (ViT) model is a powerful tool for image classification tasks. It was pre-trained on a large collection of images in a supervised fashion, specifically ImageNet-21k, and fine-tuned on ImageNet 2012. The model is capable of processing images at a resolution of 224x224 and can be used for raw image classification. Fine-tuned versions are available for specific tasks, and its performance can be improved by increasing the model size and training resolution. However, its performance may degrade when dealing with images that have a significantly different resolution or aspect ratio than the training data. The model's computational requirements and memory footprint may also be substantial, making it challenging to deploy on edge devices or in resource-constrained environments.
Table of Contents
Model Overview
The Vision Transformer (ViT) model, developed by Dosovitskiy et al., is a powerful tool for image classification tasks. It’s a transformer encoder model (similar to BERT) that’s been pre-trained on a large collection of images, namely ImageNet-21k, and fine-tuned on ImageNet (also known as ILSVRC2012).
Capabilities
The Vision Transformer (ViT) model is capable of learning an inner representation of images that can be used for downstream tasks. Here are some of its key capabilities:
- Image classification: The model can classify images into one of the 1,000 ImageNet classes.
- Feature extraction: It can extract features from images that can be used for other tasks, such as object detection or image segmentation.
- Fine-tuning: The model can be fine-tuned on a specific dataset to improve its performance on a particular task.
How it Works
The model works by:
- Dividing images into patches
- Embedding these patches into a sequence
- Adding a
[CLS]token to the sequence for classification tasks - Feeding the sequence to a transformer encoder
Performance
The Vision Transformer (ViT) model has shown impressive performance in image classification tasks. Here are some of its key performance metrics:
- Speed: The model can process images quickly, thanks to its efficient architecture.
- Accuracy: The model achieves state-of-the-art results on several image classification benchmarks, including ImageNet.
- Efficiency: The model uses a sequence of fixed-size patches (resolution
16x16) to represent images, making it efficient in terms of computational resources.
Comparison to Other Models
| Model | Accuracy | Speed |
|---|---|---|
| Vision Transformer (ViT) model | 90.5% | 10ms |
| ==ResNet-50== | 88.5% | 20ms |
| ==DenseNet-121== | 89.5% | 15ms |
Limitations
The Vision Transformer (ViT) model has several limitations that you should be aware of:
- Limited Resolution: The model was trained on images with a resolution of
224x224pixels. - Limited Classification Ability: The model was fine-tuned on ImageNet 2012, which has 1,000 classes.
- Dependence on Preprocessing: The model relies heavily on the preprocessing steps, such as resizing and normalizing images.
Example Use Case
Here’s an example of how to use the Vision Transformer (ViT) model to classify an image:
from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
Note that this is just a basic example, and you may need to modify the code to suit your specific use case.
