Skip to content

Ultralytics

The EdgeFirst Studio Model Zoo includes the Ultralytics YOLO which is a popular implementation of the ubiquitous YOLO architecture for one-shot detection models, and capable of being applied to various other tasks such as instance segmentation. This document describes the integration into EdgeFirst Studio, supported features, and optimized deployment strategies. For further details of the Ultralytics implementation of the YOLO architecture, please refer to their documentation.

EdgeFirst Studio uses the EdgeFirst fork of Ultralytics available at github.com/EdgeFirstAI/ultralytics (branch: edgefirst), which includes Camera Adaptor integration for native camera format support during training.

Our Model Zoo ecosystem provides a collection of models to be re-trained through EdgeFirst Studio and deployed to a wide range of devices using a consistent workflow to achieve the best performance and latency at the edge.

Getting Started

YOLOv5, YOLOv8, YOLO11, and YOLO26 can be trained in EdgeFirst Studio using a Graphical User Interface by following four simple steps:

  1. Select Ultralytics within the available training frameworks.

Select Ultralytics Training Framework

Next → 2. Name Session

  1. Set a name and description (optional) for the training session.

Set Name and Description

1. Select Framework ← Back Next → 3. Select Dataset

  1. Choose your dataset.

Select a Dataset

2. Name Session ← Back Next → 4. Configure & Train

  1. Configure model parameters (architecture, input size, epochs, etc.) and start Training.

Configure and Train

3. Select Dataset ← Back Next → 5. Validate

Important

Datasets and default weights are handled internally by EdgeFirst Studio. There's no need to migrate or store data locally.

Supported Versions

EdgeFirst Studio supports the following Ultralytics YOLO versions:

Version Architecture Key Features
YOLOv5 C3 backbone Classic anchor-based detection
YOLOv8 C2f backbone Anchor-free detection with DFL
YOLO11 C3k2, C2PSA Efficient architecture with depthwise convolutions
YOLO26 C3k2, A2C2f Latest architecture with area-attention

All versions share the same anchor-free Detect head and use the same decoder at inference time. See Model Metadata for details on how the decoder works across versions.

Camera Adaptor

The Camera Adaptor dropdown is available when configuring a training session, allowing you to select the target camera format for your deployment platform. This trains the model to accept native camera output (BGR, RGBA, YUYV, etc.) without runtime conversion.

See Camera Adaptor for details on supported formats and platform guidance.

Custom Models

If you have a custom float model, it is highly recommended to export to a quantized model to deploy and maximize the model's performance using the target's NPU. In this section, we will show examples of quantizing float ONNX to a TFLite. We will also be showing examples of deploying a quantized ONNX or TFLite in the NXP i.MX 8M Plus EVK using the NPU execution providers from onnxruntime or the OpenVX delegate for tflite-runtime to deploy the model in the NPU.

Deploying a quantized TFLite on the i.MX 95 EVK requires an extra step of converting the model using NXP's eIQ neutron converter. This will allow the model to be deployed on the i.MX 95 using the Neutron delegate in the platform.

Specific BSP versions required

The latest BSP available for the Maivin lacks the onnxruntime library needed to run ONNX on the NPU.

On the i.MX 8M Plus EVK, BSP v5.15 are used in the examples which was noted to have the providers needed for ONNX to run on the NPU NnapiExecutionProvider, VsiNpuExecutionProvider. Later BSPs such as v6.12 did not have these providers. This still needs confirmation from NXP as to why these providers were removed.

License

Ultralytics YOLO is covered by the GNU AGPL-3.0 license which allows for free use of this model within the constraints of the license. Ultralytics offers commercial licensing options.