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图像分类器

使用 AI 置信度分数对图像内容进行分类。

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AI-powered image classification running entirely in your browser. Models download once and work offline. Paste images with Ctrl+V.

Model

MobileViT Small · ~22 MB · Will download on first use

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<iframe src="https://optipix.art/embed/image-classifier" width="100%" height="600" style="border:1px solid #e4e4e7;border-radius:8px;" title="图像分类器 by OptiPix" loading="lazy"></iframe>
<p style="font-size:12px">Free tool by <a href="https://optipix.art/image-classifier">OptiPix 图像分类器</a></p>

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About 图像分类器

Last updated: May 2026

OptiPix Image Classifier uses a Vision Transformer (ViT) AI model to classify the content of images and provide top predictions with confidence scores. The model is trained on ImageNet with over 1,000 categories covering everyday objects, animals, plants, food, vehicles, landmarks, and more. Simply drop an image and the classifier will provide its top 5 predictions ranked by confidence, displayed as a clean bar chart. This is useful for understanding what AI sees in your images, auto-tagging photo collections, educational exploration of computer vision, and quick image categorization. The ViT model runs entirely in your browser using the Hugging Face Transformers.js library with ONNX Runtime. The model downloads once (approximately 85 MB) and is cached for offline use. Classification is fast, typically completing in 1-3 seconds on modern devices.

How It Works

The tool uses a Vision Transformer (ViT) model from the Hugging Face Transformers.js library. The image is preprocessed into a fixed-size tensor, processed through the transformer encoder, and the output is mapped to ImageNet categories with softmax confidence scores.

Use Cases

  • •Auto-tag photo collections with content labels
  • •Understand what AI sees in ambiguous images
  • •Educational exploration of computer vision models
  • •Quick image categorization for organizing large libraries
  • •Content analysis for accessibility descriptions

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If you find 图像分类器 useful, check out these related tools: Object Detection, Depth Estimation, and Image Captioner. All tools run entirely in your browser with no uploads or signups required.

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Frequently Asked Questions

How many categories can it recognize?
The model is trained on ImageNet with over 1,000 categories covering a wide range of everyday objects, animals, plants, food, vehicles, and more.
How accurate is the classification?
The ViT model achieves high accuracy on standard image classification benchmarks. Accuracy depends on image quality and whether the subject matches one of the trained categories.
How long does classification take?
Typically 1-3 seconds on modern devices. The first classification may take longer as the model loads into memory.
What does the confidence score mean?
The confidence score represents how certain the model is about each prediction. Higher scores indicate greater certainty. The scores across all categories sum to 100%.
How large is the AI model?
The ViT model is approximately 85 MB. It downloads once on first use and is cached for instant offline use.

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