Detect objects in an image, on your own hardware

DETR-ResNet-50 gives accurate boxes over the 80 COCO classes; YOLOS-tiny and YOLOv10n are far smaller and fast enough to run on a webcam stream. Boxes, labels and confidence scores are drawn over the image and exportable as JSON, which makes this a quick way to sanity-check a dataset or prototype a detection idea before committing to a Python pipeline. Nothing is uploaded, so you can point it at internal photos.

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Frequently asked questions

Can it detect classes outside COCO?

Not these models — they are fixed to their 80 training classes. For arbitrary categories described in words, use zero-shot object detection with OWL-ViT instead.

Does it work on live video?

Yes with the tiny models. YOLOS-tiny and YOLOv10n reach usable frame rates on a discrete GPU; DETR-ResNet-50 is better suited to single images.

Models available for this task

DETR ResNet-50 — Xenova/detr-resnet-50, 167 MB, 41M params
YOLOS Tiny — Xenova/yolos-tiny, 26 MB, 6.5M params
RF-DETR Nano — onnx-community/rfdetr_nano-ONNX, 108 MB
YOLOS Small — Xenova/yolos-small, 123 MB, 30.7M params
DETR ResNet-101 — Xenova/detr-resnet-101, 243 MB, 60M params
Table Transformer — Xenova/table-transformer-detection, 116 MB, 28.8M params

Explore the rest of the playground

Vision models

Background removal — Remove a background without uploading the photo
Zero-shot object detection — Detect anything you can describe, with no training
Image classification — Classify an image with ViT, locally
Zero-shot image classification — Classify images against labels you invent, using CLIP
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

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