Mobilevit Xx Small
Are you looking for a light-weight and low-latency image classification model? The MobileViT Xx Small model is a great choice. This model combines the efficiency of MobileNetV2-style layers with the power of transformers, allowing it to process images quickly and accurately. With a top-1 accuracy of 69.0% and a top-5 accuracy of 88.9% on ImageNet-1k, this model is perfect for image classification tasks. Plus, it's pre-trained on a large dataset and can be fine-tuned for specific tasks, making it a versatile tool for a variety of applications.
Table of Contents
Model Overview
The MobileViT model is a super lightweight, fast, and powerful tool for image classification tasks. It’s like a tiny but mighty robot that can look at pictures and tell you what’s in them.
What makes it special?
- It’s a convolutional neural network that uses a special block called MobileViT-block, which combines local and global processing to understand images.
- It’s designed to be fast and efficient, making it perfect for mobile devices and other applications where speed is crucial.
- It doesn’t require any positional embeddings, which makes it even more efficient.
How does it work?
- It takes an image and breaks it down into small patches.
- It processes these patches using a transformer layer.
- It then “unflattens” the patches back into feature maps.
- It uses these feature maps to classify the image into one of 1,000 possible classes.
Capabilities
The MobileViT model is a powerful tool for image classification tasks. It’s designed to be light-weight and fast, making it perfect for use on mobile devices or in applications where speed is crucial.
What can MobileViT do?
- Image Classification: MobileViT can classify images into one of 1,000 classes, using the ImageNet-1k dataset as a reference.
- Object Detection: With its ability to process images at high speed, MobileViT can be used for object detection tasks, such as detecting objects in images or videos.
How does MobileViT work?
- Convolutional Neural Network (CNN): MobileViT uses a CNN architecture, which is a type of neural network designed for image processing tasks.
- Transformer Layers: MobileViT also uses transformer layers, which allow it to process images in a more efficient and effective way.
- No Positional Embeddings: Unlike some other models, MobileViT does not require positional embeddings, which makes it even more efficient.
Comparison to other models
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params |
|---|---|---|---|
| MobileViT-XXS | 69.0 | 88.9 | 1.3 M |
| ==MobileViT-XS== | 74.8 | 92.3 | 2.3 M |
| ==MobileViT-S== | 78.4 | 94.1 | 5.6 M |
Performance
Speed
The MobileViT model is designed to be fast and efficient. It uses a unique combination of MobileNetV2-style layers and transformer blocks to process images quickly.
Accuracy
But speed isn’t everything. How accurate is the MobileViT model, really? Let’s take a closer look at its performance on the ImageNet-1k dataset:
- Top-1 accuracy:
69.0% - Top-5 accuracy:
88.9%
Efficiency
So, how efficient is the MobileViT model? Let’s talk about its training procedure. The model was trained on a dataset of 1 million images and 1,000 classes.
Example Code
Here’s an example of how to use the MobileViT model to classify an image:
from transformers import MobileViTFeatureExtractor, MobileViTForImageClassification
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = MobileViTFeatureExtractor.from_pretrained("apple/mobilevit-xx-small")
model = MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small")
inputs = feature_extractor(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])
Limitations
Current Model has some limitations that are important to consider when using it for image classification tasks.
Limited Resolution
The Current Model was trained on images with a resolution of 256x256.
Limited Number of Parameters
The Current Model has a relatively small number of parameters (1.3M) compared to other models like ==MobileViT-XS== (2.3M) and ==MobileViT-S== (5.6M).
Limited Training Data
The Current Model was trained on ImageNet-1k, a dataset consisting of 1 million images and 1,000 classes.
Limited Support for PyTorch
Currently, both the feature extractor and model support PyTorch.
Preprocessing Requirements
The Current Model requires images to be preprocessed in a specific way, including resizing/rescaling, center-cropping, and normalizing pixels.
