Prompt-free)

Annolid supports Ultralytics YOLOE-26 segmentation models with:

CLI (recommended for reproducible runs)

Annolid exports predictions as LabelMe JSON via annolid-run predict yolo_labelme.

annolid-run predict yolo_labelme \
  --weights yoloe-26s-seg.pt \
  --source /path/to/image.jpg \
  --classes person,bus

Outputs a folder next to the source (default: /path/to/image/) containing LabelMe JSON files.

Create a JSON file like:

{
  "names": ["person", "glasses"],
  "bboxes": [
    [221.52, 405.8, 344.98, 857.54],
    [120, 425, 160, 445]
  ],
  "cls": [0, 1]
}

Then run:

annolid-run predict yolo_labelme \
  --weights yoloe-26s-seg.pt \
  --source /path/to/image.jpg \
  --visual-prompts /path/to/visual_prompts.json

If you already have a LabelMe JSON with rectangle shapes labeled with class names, you can reuse it as the prompt source:

annolid-run predict yolo_labelme \
  --weights yoloe-26s-seg.pt \
  --source /path/to/image.jpg \
  --visual-prompts-labelme /path/to/prompts.json

Prompt-free weights do not require --classes or visual prompts:

annolid-run predict yolo_labelme \
  --weights yoloe-26s-seg-pf.pt \
  --source /path/to/image.jpg

Annolid’s video inference pipeline uses annolid/segmentation/yolos.py under the hood:

Selecting YOLOE-26: pick a YOLOE-26 preset from the model dropdown (for example YOLOE-26s-seg (Prompted) or YOLOE-26s-seg (Prompt-free)).
Text prompting: put a comma-separated class list in the Text Prompt field (e.g. person,bus) before running prediction with a YOLOE-26 model.
Visual prompting: draw and label rectangle shapes on the canvas; the rectangle labels become the class names for YOLOE and Annolid converts them into YOLOE visual prompts automatically.
Prompt-free YOLOE-26: select a *-pf.pt weight; Annolid will not override the internal vocabulary with prompts.