What is an AI image classifier?
An AI image classifier is a neural network that predicts what a photo shows. This tool runs MobileNet, a compact convolutional neural network trained on the ImageNet dataset, through TensorFlow.js directly in your browser. You add photos, and the model returns a ranked list of labels with confidence scores.
How image classification works
The model resizes your image, passes it through many convolutional layers and scores each of its 1,000 object classes. The highest scores become your top predictions. Because the work happens on your device, your photos stay private, and only the model files download once from a public host. Read the MobileNet model documentation for the technical details, and explore the ImageNet dataset to see what it learned.
What you can use it for
| Use case | How the tool helps |
|---|---|
| Alt text drafts | Gives you a starting phrase that you edit for your page |
| Photo library sorting | Exports labels as CSV or JSON for tagging and filtering |
| Image SEO audits | Checks whether file content matches the file name and page topic |
| Learning and demos | Shows how on-device machine learning behaves |
How to classify images in four steps
- Add one or many photos. Clear, well-lit photos with one main subject work best.
- Pick the model speed, the number of predictions and a minimum confidence.
- Click Classify All. The first run loads the model, and later runs start faster.
- Review each result, edit the draft alt text and export JSON or CSV.
Understand the limits before you trust a label
MobileNet recognizes only its 1,000 trained categories, so it cannot name a specific product, landmark or person. A 90% score means the model strongly prefers that label. It does not guarantee that the label is right. Do not use this tool for medical, legal, safety or identity decisions, and always check results that affect real people. Cropped, dark or cluttered photos lower accuracy.
Alt text and Google’s guidance on AI-assisted content
Google does not penalize content for using automation. It penalizes unhelpful content made mainly to manipulate rankings, as its guidance on generative AI content explains. Apply the same rule to alt text: write it for people, describe what matters in context and review every suggestion. See Google’s image SEO best practices for placement, file names and alt text.
Prepare your images for the web after classifying
- Shrink files with the Image Compressor before you publish.
- Resize to display dimensions with the Image Resizer.
- Create srcset and picture markup with the Responsive Image HTML Generator.
- Add a blur placeholder with the BlurHash & LQIP Generator.
- Switch formats with the All-in-One Image Converter.
Frequently asked questions
What is an AI image classifier?
An AI image classifier is a neural network that looks at a picture and predicts what it shows, such as a tabby cat, a sports car or a pizza. It returns a ranked list of labels with a confidence score for each.
How does this tool classify images?
The tool runs MobileNet, a lightweight convolutional neural network trained on the ImageNet dataset, through TensorFlow.js in your browser. It can recognize 1,000 everyday object categories.
Are my images uploaded to a server?
No. Your images are analyzed locally in your browser. Only the model files download from a public host the first time you classify, and your browser caches them afterward.
How accurate is the classification?
MobileNet performs well on clear photos of common objects and poorly on abstract, crowded or unusual scenes. A confidence score shows how strongly the model prefers a label. It does not prove that the label is correct.
Can it recognize people, faces or text?
No. The model has no face or identity recognition and no text reading. It only predicts one of its 1,000 object classes, so never use it for decisions about people.
Can I use the suggested alt text directly?
Treat it as a draft. Google asks for useful, accurate alt text written for people, so review each suggestion and describe the image in the context of your page.