Classify images against labels you invent, using CLIP

CLIP embeds images and text into the same space, so you can score a photo against any list of descriptions you write — "a product photo on a white background", "a blurry screenshot", "a person wearing glasses" — with no training data at all. SigLIP is the stronger successor and TinyCLIP the small fast option. This is the practical tool for tagging, filtering and de-duplicating an image library when your categories are specific to you and exist in no public dataset.

zero shot image classificationclip demo onlinesiglip browsercustom image labels aiimage tagging without trainingtinyclip democlip similarity tool

Frequently asked questions

Does the wording of my labels matter?

A lot. CLIP was trained on captions, so "a photo of a golden retriever" usually scores more reliably than the bare word "dog". Keep every candidate label in a consistent phrasing.

Can I use it to search an image folder?

Yes — embed each image once, keep the vectors, then compare against a text query. That is exactly how semantic image search is built, and every step of it can run here.

Models available for this task

SigLIP Base 224 — Xenova/siglip-base-patch16-224, 214 MB, 203M params
CLIP ViT-B/32 — Xenova/clip-vit-base-patch32, 157 MB, 151M params
CLIP ViT-B/16 — Xenova/clip-vit-base-patch16, 156 MB, 150M params
CLIP ViT-L/14 — Xenova/clip-vit-large-patch14, 436 MB, 428M params
TinyCLIP ViT-8M — onnx-community/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M-ONNX, 28 MB, 11M params
FashionCLIP — ff13/fashion-clip, 156 MB, 151M params

Explore the rest of the playground

Vision models

Background removal — Remove a background without uploading the photo
Object detection — Detect objects in an image, on your own hardware
Zero-shot object detection — Detect anything you can describe, with no training
Image classification — Classify an image with ViT, locally
Image segmentation — Segment an image into labelled regions, locally
Depth estimation — Generate a depth map from a single photo
Image to 3D (point cloud) — Turn a single photo into a 3D scene you can orbit
Image captioning — Generate a caption or alt text for any image
OCR (image to text) — Extract text from images without uploading them

Guides & comparisons

Model catalog — The model catalog: what actually runs in a browser
WebGPU vs WASM — WebGPU vs WebAssembly for running models in a browser
Browser AI vs cloud API — Browser inference vs a hosted API: an honest comparison
Transformers.js vs WebLLM — Transformers.js vs WebLLM
Whisper model sizes — Choosing a Whisper size for browser transcription
fp32 vs q8 vs q4 — Picking a quantization level for browser inference
Why no image generation — Why this playground has no text-to-image generation
FAQ — Frequently asked questions about browser-based AI
About — About this project

This playground needs JavaScript and WebGPU. Models are downloaded to your browser and executed on your own GPU, so there is nothing to show without a script engine. Enable JavaScript, or read the FAQ for what the site does.