If you’re looking to perform efficient image classification using the Apple MobileViT model within a JavaScript environment, you’re in the right place. This guide will walk you through the process step-by-step, ensuring that you can harness the power of the Apple MobileViT-X-Small model with ONNX weights while utilizing the Transformers.js library.
Step 1: Install Transformers.js
To begin your journey, install the @xenovatransformers library from NPM. Open your terminal and execute the following command:
npm i @xenovatransformersStep 2: Load the Model
After the installation, you can load the model to create an image classification pipeline. Here’s a snippet of how you can do this:
import pipeline from '@xenovatransformers';
// Create an image classification pipeline
const classifier = await pipeline('image-classification', 'Xenova/mobilevit-x-small', {
quantized: false,
});Step 3: Classify an Image
With the classifier ready, you can classify an image. Here’s how to do it using an image URL:
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
const output = await classifier(url);
console.log(output); // Expected output: [ { label: 'tiger', Panthera tigris', score: 0.8842423558235168 } ]Understanding the Code: An Analogy
Think of creating this image classification setup like baking a cake. Each ingredient represents a line of code that contributes to the final product. Just as you need quality flour (the model) and eggs (the library) to make a delicious cake, you need the correct model and an efficient library (Transformers.js) to classify images effectively. The process of mixing these ingredients (code) ensures that your cake (image classifier) turns out perfectly.
Troubleshooting Tips
- Issue: Model fails to load
Ensure that you’ve installed the library correctly and that the model name is accurate.
- Issue: Classification returns errors
Check that the image URL is valid and accessible. Also, ensure that the model is still supported at the given path.
- Issue: Unexpected output
Review your image source or adjust the parameters in the pipeline to optimize classification accuracy.
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Additional Notes
The use of a separate repository for ONNX weights is designed to be a temporary solution until WebML gains traction. If you wish to make your models web-ready, consider converting them to ONNX using Optimum and structuring your repository accordingly to hold the ONNX weights in a subfolder named ‘onnx’.
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.