> For the complete documentation index, see [llms.txt](https://docs.augelab.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.augelab.com/function-blocks/blocks-reference/ai-blocks/u2net-segmentation.md).

# U2Net Segmentation

This function block performs salient object detection and background removal. Use it to extract a clear foreground mask and to produce a segmented image where the background is removed.

## 📥 Inputs <a href="#inputs" id="inputs"></a>

`Image Any` The input image to analyze (color or grayscale). Connect the image source you want to segment.

## 📤 Outputs <a href="#outputs" id="outputs"></a>

`Image Gray` Grayscale probability map / mask showing object likelihood.

`Image Any` Segmented image where background is removed using the mask.

## 🕹️ Controls <a href="#controls" id="controls"></a>

`Model` Select between available U2Net variants (for example "U2Net (High Quality)" or "U2NetP (Fast)"). Choose higher quality for better masks or the fast model for speed.

`Input Size` Resize the model input. Smaller values increase speed; larger values improve detail at the cost of performance.

`Threshold` Controls binarization of the probability map to create the final mask. Increasing the threshold makes the mask stricter (fewer foreground pixels).

## 🎨 Features <a href="#features" id="features"></a>

* Foreground extraction to produce a soft probability map and a binary mask.
* Segmented color image output that keeps only the detected foreground.
* Two model choices to trade off accuracy vs speed.
* Adjustable preprocessing size and threshold for fine control.

## 📝 Usage Instructions <a href="#usage" id="usage"></a>

1. Connect an image source to the `Image Any` input.
2. Choose a `Model` depending on whether you prefer quality or speed.
3. Adjust `Input Size` to balance detail and processing time.
4. Tune `Threshold` to get the desired binary mask (preview using a `Show Image` block).
5. Use the `Image Gray` output when you need the mask for further processing, and the `Image Any` output for visualization or saving.

## 📊 Evaluation <a href="#evaluation" id="evaluation"></a>

When run, this block produces a probability mask and a segmented image based on the currently selected model and slider settings. Use the mask output to feed subsequent processing blocks (e.g., filtering, contour detection, ROI operations).

## 💡 Tips and Tricks <a href="#tips-and-tricks" id="tips-and-tricks"></a>

* To speed up processing while keeping acceptable quality, reduce `Input Size` and use the `U2NetP (Fast)` model.
* For better-looking masks on noisy images, pass the source through `Denoising` or `Blur` before this block.
* If the subject is small relative to the image, use `Image Resize` to upsample the region of interest before segmentation.
* Preview results interactively by connecting the segmented output to `Show Image`.
* Save results automatically by linking the segmented image to `Image Logger` or `Image Write`.
* If you need alternative background removal approaches, compare results with `Background Removal (RMBG-1.4)` or `Background Removal (BiRefNet)` to see which fits your scene best.
* Use the binary mask output as input to ROI or shape-analysis blocks (for example, contour finders or measurement blocks) to extract object geometry.

(hint: useful companion blocks — `Show Image`, `Image Resize`, `Denoising`, `Image Logger`, `Image Write`, `Background Removal (RMBG-1.4)`, `Background Removal (BiRefNet)`)

## 🛠️ Troubleshooting <a href="#troubleshooting" id="troubleshooting"></a>

* If masks look incomplete or too noisy: increase `Input Size` or apply `Denoising`/`Blur` to the input image.
* If the mask is too permissive (background included): raise `Threshold` to make the mask stricter.
* If processing is too slow: switch to the faster model option and/or lower the `Input Size`.
* If no output appears: confirm a valid image is connected to the `Image Any` input and preview using `Show Image`.