AI Image Classifier
Classify images with AI directly in your browser. Drop a photo and get top predictions with confidence scores using MobileNetV2, no server needed.
About AI Image Classifier
This AI image classifier runs a neural network entirely in your browser to identify what is in a photo. Upload or drag an image and the tool returns its top 5 predictions with confidence scores, all without sending any data to a server.
How it works
The tool uses MobileNetV2, a compact image classification model trained on the ImageNet dataset. It loads via Transformers.js and runs inference using WebAssembly, so no GPU or backend server is required. The model recognizes around 1,000 categories, from dogs and cats to cars, food, and furniture. On first use, the browser downloads about 13 MB of model weights. After that, everything is cached locally.
Reading the results
Each prediction shows a label and a confidence percentage. The top result is highlighted with a colored bar, while lower-ranked predictions appear faded. A high confidence score (above 70%) typically means the model is fairly sure about its answer. Lower scores suggest the image might be ambiguous or fall outside the model's training data. If you are working with AI models and want to understand their resource requirements, the AI model size calculator can help estimate memory needs.
Use cases
Developers can use this tool to quickly test how a MobileNet model handles different images before building classification features into their own apps. Photographers and content creators can use it to auto-tag images. Students exploring machine learning can see neural network predictions in real time without writing any code. For estimating token costs when working with multimodal AI APIs, try the AI token counter.
All processing happens on your device using WebAssembly. No images are uploaded or stored anywhere.
Frequently Asked Questions
How does the AI image classifier work?
The tool loads a MobileNetV2 neural network directly in your browser using Transformers.js. When you upload an image, the model analyzes it locally on your device and returns its top 5 predictions with confidence percentages. No data is sent to any server.
What kinds of images can it classify?
MobileNetV2 is trained on ImageNet, which covers about 1,000 common categories including animals, vehicles, household objects, food, plants, and more. It works best with clear photos containing a single prominent subject. Abstract art, text-heavy images, or very niche subjects may produce less accurate results.
Why does the first classification take longer?
On your first use, the browser needs to download the MobileNetV2 model, which is roughly 13 MB. After that initial download the model is cached in your browser, so subsequent visits and classifications are much faster.
Is my image sent to a server?
No. Everything runs locally in your browser using WebAssembly. Your images never leave your device, making this tool completely private.
How accurate are the predictions?
MobileNetV2 is a lightweight model optimized for speed. It handles common everyday objects well, but it is less accurate than larger models. If the confidence score for the top prediction is low, the model may be uncertain about the image content. Try a clearer or closer photo for better results.