Yoloe26 L Seg

--- license: agpl-3.0 pipeline_tag: zero-shot-object-detection library_name: yolov26 --- # YOLOE-26L-seg 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. ## Model Details - **Parameters**: 32.3M - **FLOPs**: 88.3B - **mAP^minival_50-95 (e2e)**: 36.8 / 33.7 - **mAP^minival_50-95**: 37.8 / 36.3 - **Input Size**: 640x640 ## Usage Install ultralytics with `pip install ultralytics`. Download the model: ```python from huggingface_hub import hf_hub_download model_path = hf_hub_download(repo_id="openvision/yoloe26-l-seg", filename="model.pt") ``` Infer: ```python from ultralytics import YOLO from PIL import Image import requests model = YOLO(model_path) url = 'http://images.cocodataset.org/val2017/000000039769.jpg' names = ["striped cat"] image = Image.open(requests.get(url, stream=True).raw) model.set_classes(names, model.get_text_pe(names)) results = model.predict(image) results[0].show() ``` ## Documentation For more information, visit the [official YOLO26 documentation](https://docs.ultralytics.com/models/yolo26/). ## License This model is released under the AGPL-3.0 license.