How do I detect objects in an image?

Open the AI Object Detection tool and load a JPEG, PNG, or WebP image by dragging it onto the upload zone, browsing, or pasting it. On first use the AI model (~6 MB) downloads once and is cached for offline use, then detection runs automatically: the model draws colour-coded bounding boxes around each object and labels all 80 COCO categories with confidence scores, listed by class with per-class counts. Then click Download to save the annotated image, or Export JSON to save the raw detection data — it is free forever, runs entirely in your browser, and your images are never uploaded.

Is the AI Object Detection tool free?

Yes — detecting and labelling objects in images with AI bounding boxes across 80 COCO categories is completely free, forever. No account, no sign-up, and no per-image charge. The object detection model runs entirely on your device via WebAssembly; your images are never uploaded to any server. The site is ad-supported.

What is AI object detection?

AI object detection uses a convolutional neural network (CNN) to scan an image, identify and locate objects within it, and output a class label, confidence score, and bounding box coordinates for each detected object. Common model architectures include the YOLO family and SSD.

Does the tool upload my image to a server?

Never. The AI model runs in your browser via WebAssembly and images never leave your device — this is enforced in code by a runtime network guard that blocks any attempt to send your file off-device. You can verify it yourself: open DevTools → Network and watch — nothing is uploaded.

Does it require an internet connection?

Only for the first-time model download (~6 MB). Once downloaded the model is stored in the browser Cache API and object detection works fully offline, even without a network connection.

How long does the first run take?

The first run has two stages: (1) model download (roughly 4–20 seconds depending on connection speed) and (2) ONNX Runtime initialisation (1–2 seconds). After that, inference per image takes about 0.5–5 seconds depending on image size and device performance. Subsequent runs skip the download entirely.

Can I use it offline?

Yes. After the first download the model is stored in the browser Cache API and object detection runs with no network connection. Even after closing and reopening the tab the model stays cached.

Which object categories can be detected?

The default model is trained on the COCO dataset and can recognise 80 common object categories, including people, cars, animals, appliances, and food. The full category list is shown in the results panel.

How much storage space does the AI model use?

The object detection model is approximately 6 MB. It is downloaded once and cached by the browser Cache API for offline use.

What does the confidence score mean?

The confidence score (0%–100%) represents how certain the model is about a detection. A higher score means the model is more confident that the detected class is actually present at that location. Results above 50% are generally considered reliable.

AI Object Detection — Identify and Label Objects in Images

Detect Objects identifies and labels every object in a photo with colour-coded bounding boxes and confidence scores, covering 80 COCO categories. The AI model runs entirely on your own device via WebAssembly — your images are never uploaded. The model downloads once (~6 MB), is cached in your browser, and runs fully offline.

AI object detection — automatically identifies and labels objects with bounding boxes, 80 COCO categories, model runs locally, images never uploaded.

100% Free — Forever. No fees, ever.
Private: files are processed locally in your browser and never sent to a server.
No upload, no account, no sign-up.

Open the object detection tool
Visit the AI Object Detection page. No sign-up or installation is required.
Load your image
Drag and drop a JPEG, PNG, or WebP image onto the upload zone, click to browse, or paste with Ctrl+V / Cmd+V.
Wait for the AI model to load
On first use, the object detection model (~6 MB) is downloaded to your browser and cached for offline use.
Detection runs automatically
As soon as you load an image, the AI analyses it and draws colour-coded bounding boxes around each detected object, labelling it with a class name and confidence score. Everything runs on your device — the image is never uploaded.
Review the results
Detected objects are listed by class with per-class counts (e.g. person ×3, car ×2), each with its confidence percentage.
Download the annotated image or JSON
Click Download to save the image with bounding-box annotations, or click Export JSON to save the raw detection data (labels, confidence, pixel boxes) for further processing.

How do I detect objects in an image?: Open the AI Object Detection tool and load a JPEG, PNG, or WebP image by dragging it onto the upload zone, browsing, or pasting it. On first use the AI model (~6 MB) downloads once and is cached for offline use, then detection runs automatically: the model draws colour-coded bounding boxes around each object and labels all 80 COCO categories with confidence scores, listed by class with per-class counts. Then click Download to save the annotated image, or Export JSON to save the raw detection data — it is free forever, runs entirely in your browser, and your images are never uploaded.
Is the AI Object Detection tool free?: Yes — detecting and labelling objects in images with AI bounding boxes across 80 COCO categories is completely free, forever. No account, no sign-up, and no per-image charge. The object detection model runs entirely on your device via WebAssembly; your images are never uploaded to any server. The site is ad-supported.
What is AI object detection?: AI object detection uses a convolutional neural network (CNN) to scan an image, identify and locate objects within it, and output a class label, confidence score, and bounding box coordinates for each detected object. Common model architectures include the YOLO family and SSD.
Does the tool upload my image to a server?: Never. The AI model runs in your browser via WebAssembly and images never leave your device — this is enforced in code by a runtime network guard that blocks any attempt to send your file off-device. You can verify it yourself: open DevTools → Network and watch — nothing is uploaded.
Does it require an internet connection?: Only for the first-time model download (~6 MB). Once downloaded the model is stored in the browser Cache API and object detection works fully offline, even without a network connection.
How long does the first run take?: The first run has two stages: (1) model download (roughly 4–20 seconds depending on connection speed) and (2) ONNX Runtime initialisation (1–2 seconds). After that, inference per image takes about 0.5–5 seconds depending on image size and device performance. Subsequent runs skip the download entirely.
Can I use it offline?: Yes. After the first download the model is stored in the browser Cache API and object detection runs with no network connection. Even after closing and reopening the tab the model stays cached.
Which object categories can be detected?: The default model is trained on the COCO dataset and can recognise 80 common object categories, including people, cars, animals, appliances, and food. The full category list is shown in the results panel.
How much storage space does the AI model use?: The object detection model is approximately 6 MB. It is downloaded once and cached by the browser Cache API for offline use.
What does the confidence score mean?: The confidence score (0%–100%) represents how certain the model is about a detection. A higher score means the model is more confident that the detected class is actually present at that location. Results above 50% are generally considered reliable.

The COCO (Common Objects in Context) dataset contains over 330,000 images across 80 object categories and is the most widely used benchmark for training and evaluating object detection models.
The YOLO (You Only Look Once) architecture was proposed by Redmon et al. in 2016. It completes object localisation and classification in a single forward pass, making it far faster than two-stage methods.
ONNX Runtime Web supports running object detection models in the browser with a WebGL or WebGPU backend; average inference times typically range from tens of milliseconds to a few seconds.
A bounding box is expressed as (x, y, width, height) or (x1, y1, x2, y2) and covers the region of the detected object.
Browser-side object detection produces zero network round-trips, giving far stronger privacy than a cloud API — especially important for sensitive images such as faces or documents.

🎯 AI Object Detection — Identify and Label Objects in Images

AI Object Detection — Identify and Label Objects in Images