# 🚀 YOLO26 RKNN Export Adaptation > **⚡ Optimized for Rockchip NPU Performance** This repository includes optimized RKNN export support for YOLO26 models, designed for high-performance inference on Rockchip NPU devices. ## ✨ Key Features - **🎯 Raw Output Export**: Models export without post-processing (no NMS, no sigmoid, no decode) - **⚡ CPU Post-processing**: Move decode/NMS operations to CPU for better NPU utilization - **🔧 Multi-task Support**: Works with Detection, Segmentation, OBB, and Pose models ## 📋 Export Format **Detection Model Output Structure:** ``` Input: images [1, 3, 640, 640] Outputs (6 tensors for 3 detection heads): ├─ output0_reg [1, 4*reg_max, 80, 80] # Head 0 regression (raw DFL output) ├─ output0_cls [1, nc, 80, 80] # Head 0 classification (raw logits) ├─ output1_reg [1, 4*reg_max, 40, 40] # Head 1 regression ├─ output1_cls [1, nc, 40, 40] # Head 1 classification ├─ output2_reg [1, 4*reg_max, 20, 20] # Head 2 regression └─ output2_cls [1, nc, 20, 20] # Head 2 classification ``` ## 🔨 Usage ### Step 1: Export ONNX Model ```bash # Export YOLO26 model to RKNN-compatible ONNX format yolo export model=yolo26n.pt format=rknn ``` ### Step 2: Convert to RKNN Model The `rknn_export/` directory in this repository contains complete RKNN conversion tools: - `convert.py`: Conversion script from ONNX to RKNN - `datasets/`: Quantization calibration dataset #### Environment Setup **⚠️ Important**: It's recommended to create a new virtual environment, as some dependencies of rknn-toolkit2 conflict with ultralytics ```bash # Install RKNN-Toolkit2 pip install -U rknn-toolkit2 ``` #### Using the Conversion Script View help information: ```bash python rknn_export/convert.py -h ``` **Required Arguments:** - `--model-path`: Path to ONNX model file (`.onnx` file exported in Step 1) - `--platform`: Target platform, options: - `rk3562`, `rk3566`, `rk3568`, `rk3576`, `rk3588` - `rv1126b`, `rv1109`, `rv1126`, `rk1808` **Optional Arguments:** - `--dtype`: Quantization data type (default: `i8`) - `i8` or `fp`: For `rk3562`, `rk3566`, `rk3568`, `rk3576`, `rk3588`, `rv1126b` - `u8` or `fp`: For `rv1109`, `rv1126`, `rk1808` - `--rknn-path`: Output path for RKNN model (default: `./.rknn`) - `--data-path`: Path to quantization calibration dataset (default: `datasets/COCO/coco_subset_20.txt`) - For custom data, prepare a txt file containing image paths - `--batch-size`: Batch size (default: `1`) - Can be adjusted based on NPU cores (e.g., RK3588 has 3 cores, can set to 3) - ⚠️ Note: This parameter will fix the model output dimensions #### Example Commands ```bash # Basic conversion (RK3588 platform, INT8 quantization) python rknn_export/convert.py \ --model-path best.onnx \ --platform rk3588 \ --dtype i8 # Specify output path and quantization dataset python rknn_export/convert.py \ --model-path yolo26n.onnx \ --platform rk3588 \ --dtype i8 \ --rknn-path ./models/yolo26n_rk3588.rknn \ --data-path ./my_dataset/images.txt # Multi-core batch processing (RK3588) python rknn_export/convert.py \ --model-path best.onnx \ --platform rk3588 \ --dtype i8 \ --batch-size 3 ``` Upon completion, it will display: ``` rknn model saved to: ./best.rknn ``` For more deployment examples, refer to: [RKNN Model Zoo](https://github.com/airockchip/rknn_model_zoo/tree/main/examples/) ## 📝 Implementation Details ### Modified Files - **`ultralytics/engine/exporter.py`**: Enhanced `export_rknn()` method - Uses optimal ONNX opset version - Embeds all weights in single file - Sets meaningful output tensor names - **`ultralytics/nn/modules/head.py`**: Updated `Detect`, `Segment`, `OBB`, `Pose` classes - Added RKNN-specific forward logic - Returns raw predictions without activation functions - **`ultralytics/nn/autobackend.py`**: Added RKNN inference support notes ### Training & Inference - ✅ **Training**: Not affected - all modifications only apply during export - ✅ **Standard Export**: Other export formats (ONNX, TensorRT, etc.) work as before - ✅ **RKNN Export**: Special handling only when `format=rknn` ## 🎯 Performance Benefits - **Faster Inference**: Post-processing on CPU is faster than on NPU for models - **Better NPU Utilization**: NPU focuses on backbone and head computations - **Flexible Deployment**: Easy to customize post-processing logic ---

Ultralytics YOLO banner

[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)
Ultralytics CI Ultralytics Downloads Ultralytics Discord Ultralytics Forums Ultralytics Reddit
Run Ultralytics on Gradient Open Ultralytics In Colab Open Ultralytics In Kaggle Open Ultralytics In Binder

[Ultralytics](https://www.ultralytics.com/) creates cutting-edge, state-of-the-art (SOTA) [YOLO models](https://www.ultralytics.com/yolo) built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are **fast**, **accurate**, and **easy to use**. They excel at [object detection](https://docs.ultralytics.com/tasks/detect/), [tracking](https://docs.ultralytics.com/modes/track/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [image classification](https://docs.ultralytics.com/tasks/classify/), and [pose estimation](https://docs.ultralytics.com/tasks/pose/) tasks. Find detailed documentation in the [Ultralytics Docs](https://docs.ultralytics.com/). Get support via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose). Join discussions on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/)! Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license). YOLO26 performance plots
Ultralytics GitHub space Ultralytics LinkedIn space Ultralytics Twitter space Ultralytics YouTube space Ultralytics TikTok space Ultralytics BiliBili space Ultralytics Discord
## 📄 Documentation See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full [Ultralytics Docs](https://docs.ultralytics.com/).
Install Install the `ultralytics` package, including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml), in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). [![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/) [![Ultralytics Downloads](https://static.pepy.tech/badge/ultralytics)](https://clickpy.clickhouse.com/dashboard/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/) ```bash pip install ultralytics ``` For alternative installation methods, including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and building from source via Git, please consult the [Quickstart Guide](https://docs.ultralytics.com/quickstart/). [![Conda Version](https://img.shields.io/conda/vn/conda-forge/ultralytics?logo=condaforge)](https://anaconda.org/conda-forge/ultralytics) [![Docker Image Version](https://img.shields.io/docker/v/ultralytics/ultralytics?sort=semver&logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics) [![Ultralytics Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics)
Usage ### CLI You can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the `yolo` command: ```bash # Predict using a pretrained YOLO model (e.g., YOLO26n) on an image yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg' ``` The `yolo` command supports various tasks and modes, accepting additional arguments like `imgsz=640`. Explore the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli/) for more examples. ### Python Ultralytics YOLO can also be integrated directly into your Python projects. It accepts the same [configuration arguments](https://docs.ultralytics.com/usage/cfg/) as the CLI: ```python from ultralytics import YOLO # Load a pretrained YOLO26n model model = YOLO("yolo26n.pt") # Train the model on the COCO8 dataset for 100 epochs train_results = model.train( data="coco8.yaml", # Path to dataset configuration file epochs=100, # Number of training epochs imgsz=640, # Image size for training device="cpu", # Device to run on (e.g., 'cpu', 0, [0,1,2,3]) ) # Evaluate the model's performance on the validation set metrics = model.val() # Perform object detection on an image results = model("path/to/image.jpg") # Predict on an image results[0].show() # Display results # Export the model to ONNX format for deployment path = model.export(format="onnx") # Returns the path to the exported model ``` Discover more examples in the YOLO [Python Docs](https://docs.ultralytics.com/usage/python/).
## ✨ Models Ultralytics supports a wide range of YOLO models, from early versions like [YOLOv3](https://docs.ultralytics.com/models/yolov3/) to the latest [YOLO26](https://docs.ultralytics.com/models/yolo26/). The tables below showcase YOLO26 models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset for [Detection](https://docs.ultralytics.com/tasks/detect/), [Segmentation](https://docs.ultralytics.com/tasks/segment/), and [Pose Estimation](https://docs.ultralytics.com/tasks/pose/). Additionally, [Classification](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset are available. [Tracking](https://docs.ultralytics.com/modes/track/) mode is compatible with all Detection, Segmentation, and Pose models. All [Models](https://docs.ultralytics.com/models/) are automatically downloaded from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) upon first use. Ultralytics YOLO supported tasks

Detection (COCO) Explore the [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples. These models are trained on the [COCO dataset](https://cocodataset.org/), featuring 80 object classes. | Model | size
(pixels) | mAPval
50-95 | mAPval
50-95(e2e) | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(B) | | ------------------------------------------------------------------------------------ | --------------------------- | -------------------------- | ------------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- | | [YOLO26n](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt) | 640 | 40.9 | 40.1 | 38.9 ± 0.7 | 1.7 ± 0.0 | 2.4 | 5.4 | | [YOLO26s](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s.pt) | 640 | 48.6 | 47.8 | 87.2 ± 0.9 | 2.5 ± 0.0 | 9.5 | 20.7 | | [YOLO26m](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m.pt) | 640 | 53.1 | 52.5 | 220.0 ± 1.4 | 4.7 ± 0.1 | 20.4 | 68.2 | | [YOLO26l](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l.pt) | 640 | 55.0 | 54.4 | 286.2 ± 2.0 | 6.2 ± 0.2 | 24.8 | 86.4 | | [YOLO26x](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x.pt) | 640 | 57.5 | 56.9 | 525.8 ± 4.0 | 11.8 ± 0.2 | 55.7 | 193.9 | - **mAPval** values refer to single-model single-scale performance on the [COCO val2017](https://cocodataset.org/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details.
Reproduce with `yolo val detect data=coco.yaml device=0` - **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export.
Reproduce with `yolo val detect data=coco.yaml batch=1 device=0|cpu`
Segmentation (COCO) Refer to the [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage examples. These models are trained on [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/), including 80 classes. | Model | size
(pixels) | mAPbox
50-95(e2e) | mAPmask
50-95(e2e) | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(B) | | -------------------------------------------------------------------------------------------- | --------------------------- | ------------------------------- | -------------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- | | [YOLO26n-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-seg.pt) | 640 | 39.6 | 33.9 | 53.3 ± 0.5 | 2.1 ± 0.0 | 2.7 | 9.1 | | [YOLO26s-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-seg.pt) | 640 | 47.3 | 40.0 | 118.4 ± 0.9 | 3.3 ± 0.0 | 10.4 | 34.2 | | [YOLO26m-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-seg.pt) | 640 | 52.5 | 44.1 | 328.2 ± 2.4 | 6.7 ± 0.1 | 23.6 | 121.5 | | [YOLO26l-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-seg.pt) | 640 | 54.4 | 45.5 | 387.0 ± 3.7 | 8.0 ± 0.1 | 28.0 | 139.8 | | [YOLO26x-seg](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-seg.pt) | 640 | 56.5 | 47.0 | 787.0 ± 6.8 | 16.4 ± 0.1 | 62.8 | 313.5 | - **mAPval** values are for single-model single-scale on the [COCO val2017](https://cocodataset.org/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details.
Reproduce with `yolo val segment data=coco.yaml device=0` - **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export.
Reproduce with `yolo val segment data=coco.yaml batch=1 device=0|cpu`
Classification (ImageNet) Consult the [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples. These models are trained on [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/), covering 1000 classes. | Model | size
(pixels) | acc
top1 | acc
top5 | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(B) at 224 | | -------------------------------------------------------------------------------------------- | --------------------------- | ---------------------- | ---------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ------------------------------ | | [YOLO26n-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-cls.pt) | 224 | 71.4 | 90.1 | 5.0 ± 0.3 | 1.1 ± 0.0 | 2.8 | 0.5 | | [YOLO26s-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-cls.pt) | 224 | 76.0 | 92.9 | 7.9 ± 0.2 | 1.3 ± 0.0 | 6.7 | 1.6 | | [YOLO26m-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-cls.pt) | 224 | 78.1 | 94.2 | 17.2 ± 0.4 | 2.0 ± 0.0 | 11.6 | 4.9 | | [YOLO26l-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-cls.pt) | 224 | 79.0 | 94.6 | 23.2 ± 0.3 | 2.8 ± 0.0 | 14.1 | 6.2 | | [YOLO26x-cls](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-cls.pt) | 224 | 79.9 | 95.0 | 41.4 ± 0.9 | 3.8 ± 0.0 | 29.6 | 13.6 | - **acc** values represent model accuracy on the [ImageNet](https://www.image-net.org/) dataset validation set.
Reproduce with `yolo val classify data=path/to/ImageNet device=0` - **Speed** metrics are averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export.
Reproduce with `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
Pose (COCO) See the [Pose Estimation Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples. These models are trained on [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/), focusing on the 'person' class. | Model | size
(pixels) | mAPpose
50-95(e2e) | mAPpose
50(e2e) | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(B) | | ---------------------------------------------------------------------------------------------- | --------------------------- | -------------------------------- | ----------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- | | [YOLO26n-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-pose.pt) | 640 | 57.2 | 83.3 | 40.3 ± 0.5 | 1.8 ± 0.0 | 2.9 | 7.5 | | [YOLO26s-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-pose.pt) | 640 | 63.0 | 86.6 | 85.3 ± 0.9 | 2.7 ± 0.0 | 10.4 | 23.9 | | [YOLO26m-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-pose.pt) | 640 | 68.8 | 89.6 | 218.0 ± 1.5 | 5.0 ± 0.1 | 21.5 | 73.1 | | [YOLO26l-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-pose.pt) | 640 | 70.4 | 90.5 | 275.4 ± 2.4 | 6.5 ± 0.1 | 25.9 | 91.3 | | [YOLO26x-pose](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-pose.pt) | 640 | 71.6 | 91.6 | 565.4 ± 3.0 | 12.2 ± 0.2 | 57.6 | 201.7 | - **mAPval** values are for single-model single-scale on the [COCO Keypoints val2017](https://docs.ultralytics.com/datasets/pose/coco/) dataset. See [YOLO Performance Metrics](https://docs.ultralytics.com/guides/yolo-performance-metrics/) for details.
Reproduce with `yolo val pose data=coco-pose.yaml device=0` - **Speed** metrics are averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export.
Reproduce with `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
Oriented Bounding Boxes (DOTAv1) Check the [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples. These models are trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), including 15 classes. | Model | size
(pixels) | mAPtest
50-95(e2e) | mAPtest
50(e2e) | Speed
CPU ONNX
(ms) | Speed
T4 TensorRT10
(ms) | params
(M) | FLOPs
(B) | | -------------------------------------------------------------------------------------------- | --------------------------- | -------------------------------- | ----------------------------- | ------------------------------------ | ----------------------------------------- | ------------------------ | ----------------------- | | [YOLO26n-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n-obb.pt) | 1024 | 52.4 | 78.9 | 97.7 ± 0.9 | 2.8 ± 0.0 | 2.5 | 14.0 | | [YOLO26s-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s-obb.pt) | 1024 | 54.8 | 80.9 | 218.0 ± 1.4 | 4.9 ± 0.1 | 9.8 | 55.1 | | [YOLO26m-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26m-obb.pt) | 1024 | 55.3 | 81.0 | 579.2 ± 3.8 | 10.2 ± 0.3 | 21.2 | 183.3 | | [YOLO26l-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l-obb.pt) | 1024 | 56.2 | 81.6 | 735.6 ± 3.1 | 13.0 ± 0.2 | 25.6 | 230.0 | | [YOLO26x-obb](https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26x-obb.pt) | 1024 | 56.7 | 81.7 | 1485.7 ± 11.5 | 30.5 ± 0.9 | 57.6 | 516.5 | - **mAPtest** values are for single-model multiscale performance on the [DOTAv1 test set](https://captain-whu.github.io/DOTA/dataset.html).
Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to the [DOTA evaluation server](https://captain-whu.github.io/DOTA/evaluation.html). - **Speed** metrics are averaged over [DOTAv1 val images](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10) using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. CPU speeds measured with [ONNX](https://onnx.ai/) export. GPU speeds measured with [TensorRT](https://developer.nvidia.com/tensorrt) export.
Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
## 🧩 Integrations Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with partners like [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/), [Comet ML](https://docs.ultralytics.com/integrations/comet/), [Roboflow](https://docs.ultralytics.com/integrations/roboflow/), and [Intel OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow. Explore more at [Ultralytics Integrations](https://docs.ultralytics.com/integrations/). Ultralytics active learning integrations

Ultralytics Platform logo space Weights & Biases logo space Comet ML logo space Neural Magic logo
| Ultralytics Platform 🌟 | Weights & Biases | Comet | Neural Magic | | :---------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: | | Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics Platform](https://platform.ultralytics.com/ultralytics/yolo26). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.ultralytics.com/integrations/weights-biases/). | Free forever, [Comet ML](https://docs.ultralytics.com/integrations/comet/) lets you save YOLO models, resume training, and interactively visualize predictions. | Run YOLO inference up to 6x faster with [Neural Magic DeepSparse](https://docs.ultralytics.com/integrations/neural-magic/). | ## 🤝 Contribute We thrive on community collaboration! Ultralytics YOLO wouldn't be the SOTA framework it is without contributions from developers like you. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. We also welcome your feedback—share your experience by completing our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A huge **Thank You** 🙏 to everyone who contributes! [![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors) We look forward to your contributions to help make the Ultralytics ecosystem even better! ## 📜 License Ultralytics offers two licensing options to suit different needs: - **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/license/agpl-v3) open-source license is perfect for students, researchers, and enthusiasts. It encourages open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for full details. - **Ultralytics Enterprise License**: Designed for commercial use, this license allows for the seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. If your use case involves commercial deployment, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license). ## 📞 Contact For bug reports and feature requests related to Ultralytics software, please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). For questions, discussions, and community support, join our active communities on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/). We're here to help with all things Ultralytics!
Ultralytics GitHub space Ultralytics LinkedIn space Ultralytics Twitter space Ultralytics YouTube space Ultralytics TikTok space Ultralytics BiliBili space Ultralytics Discord