YOLOv8-World v2 — ONNX with dynamic text prompts
ONNX exports of Ultralytics YOLOv8-World v2 (yolov8s-worldv2.pt, yolov8l-worldv2.pt)
with a dynamic txt_feats input so class prompts can be re-encoded at runtime
(zero-shot open-vocabulary detection), rather than being baked into the graph.
Files
| File | Size | Params |
|---|---|---|
yolov8s-worldv2.onnx |
48.8 MB | 12.7 M |
yolov8l-worldv2.onnx |
178.8 MB | 46.8 M |
Input schema
Two named inputs (all axes dynamic):
images—float32[batch, 3, height, width], normalized to[0, 1], RGB, imgsz=640 recommended.txt_feats—float32[batch, num_classes, 512], CLIP ViT-B/32 text embeddings, L2-normalized (though the graph re-normalizes internally).
Output
output0—float32[batch, 4 + num_classes, num_anchors](YOLO head: 4 box regressors + per-class logits; anchors depend on input HxW).
Post-processing: apply sigmoid to the class channels, then standard NMS.
Text embedding
Use CLIP ViT-B/32 (openai/clip-vit-base-patch32) to encode each class prompt
into a 512-dim vector, stack into (1, N, 512).
Export
Exported from Ultralytics 8.4.37 with a custom torch.onnx.export path that
patches WorldDetect.forward to use torch.split sized from text.shape[1]
(so N stays dynamic). Opset 18. Simplified with onnxslim.
License
AGPL-3.0 (inherits from Ultralytics YOLOv8-World weights).