Transformer.js
#3
by
ninini1
- opened
Hello,
Thank you for your work. Do you think you can make the model usable in javascript with transformer.js or any other way?
<script type="module">
import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.1.0';
document.getElementById('runModel').addEventListener('click', async () => {
const fileInput = document.getElementById('imageInput');
if (fileInput.files.length === 0) {
alert('Veuillez sélectionner une image.');
return;
}
const imageFile = fileInput.files[0];
const imageBitmap = await createImageBitmap(imageFile);
const classifier = await pipeline('image-classification', 'umm-maybe/AI-image-detector');
const results = await classifier(imageBitmap);
document.getElementById('outputText').innerText = `Résultat : ${JSON.stringify(results, null, 2)}`;
});
</script>
Results in
Uncaught (in promise) Error: Could not locate file: "https://huggingface.co/umm-maybe/AI-image-detector/resolve/main/onnx/model_quantized.onnx".
Thank you!
By the way i tried to generate an onnx file from your network, but it gives me this error
async function runExample() {
// Create an ONNX inference session with WebGL backend.
const session = new onnx.InferenceSession({ backendHint: 'webgl' });
// Load an ONNX model. This model is Resnet50 that takes a 1*3*224*224 image and classifies it.
await session.loadModel("./models/AI-image-detector.onnx");
// Load image.
const imageLoader = new ImageLoader(imageSize, imageSize);
const imageData = await imageLoader.getImageData('./resnet-cat.jpg');
// Preprocess the image data to match input dimension requirement, which is 1*3*224*224.
const width = imageSize;
const height = imageSize;
const preprocessedData = preprocess(imageData.data, width, height);
const inputTensor = new onnx.Tensor(preprocessedData, 'float32', [1, 3, width, height]);
// Run model with Tensor inputs and get the result.
const outputMap = await session.run([inputTensor]);
const outputData = outputMap.values().next().value.data;
// Render the output result in html.
printMatches(outputData);
}
Uncaught (in promise) TypeError: int64 is not supported