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物体検出

バウンディングボックスで画像内の物体を検出し、ラベル付けします。

This tool loads a ~160 MB DETR AI model in your browser. It downloads once and is cached for offline use.

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About 物体検出

OptiPix Object Detection uses a YOLO-based AI model running in your browser to detect and label objects in your images. The model identifies common objects including people, vehicles, animals, furniture, electronics, and many more categories with bounding boxes and confidence scores. Each detected object is outlined with a colored bounding box and labeled with its category name and confidence percentage. This is useful for image analysis, content moderation, accessibility (describing image content), educational purposes, and understanding what objects are present in complex scenes. The AI model runs entirely in your browser using ONNX Runtime WebAssembly — no server uploads, complete privacy. The model downloads once and is cached for offline use. You can toggle label display on and off, and download the annotated image as a PNG file.

How It Works

The tool uses a YOLO (You Only Look Once) model running in your browser via ONNX Runtime WebAssembly. The image is preprocessed, fed through the neural network, and the output is decoded to produce bounding boxes with class labels and confidence scores.

Use Cases

  • •Analyze images for content cataloging and tagging
  • •Educational tool for learning about computer vision
  • •Accessibility: describe image contents for visually impaired users
  • •Content moderation to identify specific object types
  • •Research and data analysis of image datasets

Frequently Asked Questions

What objects can be detected?
The model can detect over 80 common object categories including people, cars, bicycles, dogs, cats, chairs, tables, laptops, phones, bottles, and many more.
How accurate is the detection?
The model provides confidence scores for each detection. Objects with high confidence (>80%) are typically accurate. Smaller or partially occluded objects may have lower confidence.
Can I hide the labels?
Yes. A toggle switch lets you show or hide labels on the detected bounding boxes while still highlighting the objects.
How many objects can be detected per image?
The model can detect multiple objects simultaneously. There is no hard limit, though performance is best with images containing fewer than 50 objects.
Does this work offline?
Yes. After the AI model downloads on first use, detection works entirely offline with no internet connection needed.

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