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Algorithms are the building blocks of your Computer Vision workflows. You can create and share your own algorithms or use the ones provided by the community on Ikomia HUB.

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Use Python for full control and complex chaining.

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Ikomia API is an open-source API to train, test, and build reusable Computer Vision workflows. You can select and chain the best state-of-the-art algorithms from your preferred frameworks such as OpenCV, Detectron2, OpenMMLab or YOLO.

Reusable Computer Vision workflows in a few lines of code.

Ikomia API

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<div align="center">
  <img src="images/icon.png" alt="Algorithm icon">
  <h1 align="center">infer_yolo_26</h1>
</div>
<br />
<p align="center">
    <a href="https://github.com/Ikomia-hub/infer_yolo_26">
        <img alt="Stars" src="https://img.shields.io/github/stars/Ikomia-hub/infer_yolo_26">
    </a>
    <a href="https://app.ikomia.ai/hub/">
        <img alt="Website" src="https://img.shields.io/website/http/app.ikomia.ai/en.svg?down_color=red&down_message=offline&up_message=online">
    </a>
    <a href="https://github.com/Ikomia-hub/infer_yolo_26/blob/main/LICENSE.md">
        <img alt="GitHub" src="https://img.shields.io/github/license/Ikomia-hub/infer_yolo_26.svg?color=blue">
    </a>    
    <br>
    <a href="https://discord.com/invite/82Tnw9UGGc">
        <img alt="Discord community" src="https://img.shields.io/badge/Discord-white?style=social&logo=discord">
    </a> 
</p>

YOLO26 object detection inference powered by Ultralytics models.

![illustration instance segmentation](https://raw.githubusercontent.com/Ikomia-hub/infer_yolo_26_seg/main/images/output.jpg)

## :rocket: Use with Ikomia API

#### 1. Install Ikomia API

We strongly recommend using a virtual environment. If you're not sure where to start, we offer a tutorial [here](https://www.ikomia.ai/blog/a-step-by-step-guide-to-creating-virtual-environments-in-python).

```sh
pip install ikomia
```

#### 2. Create your workflow


```python
from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_yolo_26", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/refs/heads/main/examples/img/img_bike_rider.jpeg")
```

## :sunny: Use with Ikomia Studio

Ikomia Studio offers a friendly UI with the same features as the API.

- If you haven't started using Ikomia Studio yet, download and install it from [this page](https://www.ikomia.ai/studio).
- For additional guidance on getting started with Ikomia Studio, check out [this blog post](https://www.ikomia.ai/blog/how-to-get-started-with-ikomia-studio).

## :pencil: Set algorithm parameters

Parameters:
- `model_name`: YOLO26 model variant (`yolo26n`, `yolo26s`, `yolo26m`, `yolo26l`, `yolo26x`).
- `cuda`: Enable CUDA if available (`True`/`False`).
- `input_size`: Inference resolution (int, e.g. `640`).
- `conf_thres`: Confidence threshold (float 0-1).
- `iou_thres`: IoU threshold for NMS (float 0-1).
- `model_weight_file`: Custom `.pt` path (empty to use default weights).


```python
from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_yolo_26", auto_connect=True)

algo.set_parameters({
    "model_name": "yolo26m",
    "cuda": "True",
    "input_size": "640",
    "conf_thres": "0.25",
    "iou_thres": "0.7",
    "model_weight_file": ""
})

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/refs/heads/main/examples/img/img_bike_rider.jpeg")
```

## :mag: Explore algorithm outputs

Every algorithm produces specific outputs, yet they can be explored them the same way using the Ikomia API. For a more in-depth understanding of managing algorithm outputs, please refer to the [documentation](https://ikomia-dev.github.io/python-api-documentation/advanced_guide/IO_management.html).

```python
from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_yolo_26", auto_connect=True)

# Run on your image  
wf.run_on(url="https://raw.githubusercontent.com/Ikomia-dev/notebooks/refs/heads/main/examples/img/img_bike_rider.jpeg")

# Iterate over outputs
for output in algo.get_outputs():
    # Print information
    print(output)
    # Export it to JSON
    output.to_json()
```

infer_yolo_26

YOLO by Ultralytics

Inference with YOLO26 models (Ultralytics)

Permissions	Conditions	Limitations
Commercial use	License and copyright notice	Liability
Modification	State changes	Warranty
Distribution	Disclose source
Patent use	Network use is distribution
Private use	Same license

infer_yolo_26

About

🚀 Use with Ikomia API

1. Install Ikomia API

2. Create your workflow

☀️ Use with Ikomia Studio

📝 Set algorithm parameters

🔍 Explore algorithm outputs

Developer

Ikomia

License

GNU Affero General Public License v3.0