> ## Documentation Index
> Fetch the complete documentation index at: https://docs.comfy.org/llms.txt
> Use this file to discover all available pages before exploring further.

# WeVisOneDetect - ComfyUI Built-in Node Documentation

> WeVisOne-VL Detect finds objects in an image from a text prompt and returns boxes, alpha mattes, points, or the model's own answer

WeVisOne-VL Detect finds objects in an image from a text prompt and returns boxes, alpha mattes, points, or the model's own answer, depending on the task you pick. It covers open-vocabulary detection and segmentation, referring expressions, pointing, counting and OCR grounding, and runs one prompt per image in the batch.

## Inputs

| Parameter | Description | Data Type | Required | Range |
| - | - | - | - | - |
| `clip` | The WeVisOne-VL text encoder that loads the checkpoint. | CLIP | Yes | - |
| `image` | The image or batch of images to search. | IMAGE | Yes | - |
| `task` | What the node should return. Segmentation returns boxes plus alpha mattes; detection, referring, ocr and ocr\_grounding return boxes; point and count\_point return points; count\_box counts with boxes; visual\_prompt finds objects similar to the reference boxes in `bboxes`; freeform sends the query unchanged, so the reply is normally text (default: "segmentation"). | COMBO | Yes | `"segmentation"` `"detection"` `"referring"` `"point"` `"count_box"` `"count_point"` `"ocr"` `"ocr_grounding"` `"visual_prompt"` `"freeform"` |
| `query` | What to find, in plain language (for example "the red mug"). `detection` and `point` read one label per line, `count_box` and `count_point` count the subject you name, `ocr_grounding` looks for this exact text, `visual_prompt` uses `bboxes` instead, and `freeform` sends it as the whole prompt. `ocr` ignores it. | STRING | Yes | - |
| `bboxes` | Reference boxes that `visual_prompt` uses as examples of the object to find. | BOUNDING\_BOX | No | - |
| `individual_masks` | Return one matte per detected box from the `bboxes` output, frame by frame, instead of one combined matte per frame (default: false). | BOOLEAN | No | true false |
| `max_length` | Maximum number of tokens the model may generate for its answer (default: 4096). | INT | Yes | 1 to 32768 |

**Note:** Alpha mattes are filled when the model answers with object references, which the `segmentation` task asks for. Other tasks normally return boxes, points or text with an empty matte, but a `freeform` prompt that asks for object references can fill them too.

## Outputs

| Output Name | Description | Data Type |
| - | - | - |
| `masks` | Alpha mattes of the matched objects, one per frame by default or one per box with `individual_masks`. | MASK |
| `bboxes` | Boxes found in each image, stored as `x`, `y`, `width`, `height` and `label`. | BOUNDING\_BOX |
| `points` | Points as JSON in pixels, one object per point with `x`, `y`, `label` and `frame`, for example `[{"x": 128, "y": 96, "label": "cup", "frame": 0}]`. Filled by the `point` and `count_point` tasks. | STRING |
| `text` | The model's raw answers, joined with newlines; an answer can itself span several lines. | STRING |

> This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! [Edit on GitHub](https://github.com/Comfy-Org/embedded-docs/blob/main/comfyui_embedded_docs/docs/WeVisOneDetect/en.md)

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