YOLOE-26 integrates the high-performance YOLO26 architecture with the open-vocabulary capabilities of the YOLOE series. It enables real-time detection and segmentation of any object class using text prompts, visual prompts, or a prompt-free mode for zero-shot inference, effectively removing the constraints of fixed-category training.
By leveraging YOLO26's NMS-free, end-to-end design, YOLOE-26 delivers fast open-world inference. This makes it a powerful solution for edge applications in dynamic environments where the objects of interest represent a broad and evolving vocabulary.
1from huggingface_hub import hf_hub_download
2
3model_path = hf_hub_download(repo_id="openvision/yoloe-26m-seg", filename="model.pt")
1from ultralytics import YOLO
2from PIL import Image
3import requests
4
5model = YOLO(model_path)
6url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
7names = ["striped cat"]
8image = Image.open(requests.get(url, stream=True).raw)
9model.set_classes(names, model.get_text_pe(names))
10
11results = model.predict(image)
12results[0].show()
For more information, visit the
official YOLO26 documentation.
This model is released under the AGPL-3.0 license.
