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README.md

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+---
+license: agpl-3.0
+pipeline_tag: object-detection
+library_name: yolov26
+---
+
+# YOLO26-L
+
+Ultralytics YOLO26 is the latest evolution in the YOLO series of real-time object detectors, engineered from the ground up for edge and low-power devices. It introduces a streamlined design that removes unnecessary complexity while integrating targeted innovations to deliver faster, lighter, and more accessible deployment.
+
+## Model Specifications
+
+| Property | Value |
+|----------|-------|
+| Input Size | 640 pixels |
+| mAP (50-95) | 55.0 |
+| mAP (50-95, e2e) | 54.4 |
+| CPU Speed (ONNX) | 286.2 ms |
+| T4 TensorRT10 Speed | 6.2 ms |
+| Parameters | 24.8M |
+| FLOPs | 86.4B |
+
+## Key Features
+
+The architecture of YOLO26 is guided by three core principles:
+
+**Simplicity:** YOLO26 is a native end-to-end model, producing predictions directly without the need for non-maximum suppression (NMS). By eliminating this post-processing step, inference becomes faster, lighter, and easier to deploy in real-world systems.
+
+**Deployment Efficiency:** The end-to-end design cuts out an entire stage of the pipeline, dramatically simplifying integration, reducing latency, and making deployment more robust across diverse environments.
+
+**Training Innovation:** YOLO26 introduces the MuSGD optimizer, a hybrid of SGD and Muon — inspired by Moonshot AI's Kimi K2 breakthroughs in LLM training. This optimizer brings enhanced stability and faster convergence.
+
+### Additional Highlights
+
+- **DFL Removal:** Simplified inference and broader hardware compatibility
+- **End-to-End NMS-Free Inference:** Reduced latency and easier production integration
+- **ProgLoss + STAL:** Improved small-object recognition
+- **Up to 43% Faster CPU Inference:** Optimized for edge computing
+
+## 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/yolo26-l", filename="model.pt")
+```
+
+Infer.
+```python
+from ultralytics import YOLO
+from PIL import Image
+import requests
+
+model = YOLO(model_path)
+
+# Train the model on the COCO8 example dataset for 100 epochs
+results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
+
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+
+# Run inference with the YOLO26l model on the image
+results = model.predict(image)
+```
+
+## Documentation
+
+For more information, see the [official YOLO26 documentation](https://docs.ultralytics.com/models/yolo26/).

model.pt

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+size 53211173