Upload yoloe-26m-seg model

README.md

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+---
+license: agpl-3.0
+pipeline_tag: zero-shot-object-detection
+library_name: yolo26
+---
+
+# YOLOE-26M-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**: 27.9M
+- **FLOPs**: 70.1B
+- **mAP<sup>minival</sup><sub>50-95</sub> (e2e)**: 35.4 / 31.3
+- **mAP<sup>minival</sup><sub>50-95</sub>**: 35.4 / 33.9
+- **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/yoloe-26m-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.

model.pt

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