dgenerate-ultralytics-headless
Advanced tools
Automatically built Ultralytics package with python-opencv-headless dependency instead of python-opencv
This repository contains a GitHub Action that automatically builds wheels for Ultralytics with OpenCV Headless dependency instead of the standard OpenCV.
The GitHub Action performs the following steps:
opencv-python with opencv-python-headless in dependency specThe standard Ultralytics package depends on python-opencv, which requires a GUI environment. This can cause issues in headless environments like servers or containers. By replacing it with python-opencv-headless, you can use Ultralytics in these environments without GUI dependencies.
To use the modified release, install it with pip:
pip install dgenerate-ultralytics-headless
Ultralytics creates cutting-edge, state-of-the-art (SOTA) YOLO models 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, tracking, instance segmentation, image classification, and pose estimation tasks.
Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums!
Request an Enterprise License for commercial use at Ultralytics Licensing.
See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full Ultralytics Docs.
Install the ultralytics package, including all requirements, in a Python>=3.8 environment with PyTorch>=1.8.
pip install ultralytics
For alternative installation methods, including Conda, Docker, and building from source via Git, please consult the Quickstart Guide.
You can use Ultralytics YOLO directly from the Command Line Interface (CLI) with the yolo command:
# Predict using a pretrained YOLO model (e.g., YOLO11n) on an image
yolo predict model=yolo11n.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 for more examples.
Ultralytics YOLO can also be integrated directly into your Python projects. It accepts the same configuration arguments as the CLI:
from ultralytics import YOLO
# Load a pretrained YOLO11n model
model = YOLO("yolo11n.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.
Ultralytics supports a wide range of YOLO models, from early versions like YOLOv3 to the latest YOLO11. The tables below showcase YOLO11 models pretrained on the COCO dataset for Detection, Segmentation, and Pose Estimation. Additionally, Classification models pretrained on the ImageNet dataset are available. Tracking mode is compatible with all Detection, Segmentation, and Pose models. All Models are automatically downloaded from the latest Ultralytics release upon first use.
Explore the Detection Docs for usage examples. These models are trained on the COCO dataset, featuring 80 object classes.
| Model | size (pixels) | mAPval 50-95 | Speed CPU ONNX (ms) | Speed T4 TensorRT10 (ms) | params (M) | FLOPs (B) |
|---|---|---|---|---|---|---|
| YOLO11n | 640 | 39.5 | 56.1 ± 0.8 | 1.5 ± 0.0 | 2.6 | 6.5 |
| YOLO11s | 640 | 47.0 | 90.0 ± 1.2 | 2.5 ± 0.0 | 9.4 | 21.5 |
| YOLO11m | 640 | 51.5 | 183.2 ± 2.0 | 4.7 ± 0.1 | 20.1 | 68.0 |
| YOLO11l | 640 | 53.4 | 238.6 ± 1.4 | 6.2 ± 0.1 | 25.3 | 86.9 |
| YOLO11x | 640 | 54.7 | 462.8 ± 6.7 | 11.3 ± 0.2 | 56.9 | 194.9 |
yolo val detect data=coco.yaml device=0yolo val detect data=coco.yaml batch=1 device=0|cpuRefer to the Segmentation Docs for usage examples. These models are trained on COCO-Seg, including 80 classes.
| Model | size (pixels) | mAPbox 50-95 | mAPmask 50-95 | Speed CPU ONNX (ms) | Speed T4 TensorRT10 (ms) | params (M) | FLOPs (B) |
|---|---|---|---|---|---|---|---|
| YOLO11n-seg | 640 | 38.9 | 32.0 | 65.9 ± 1.1 | 1.8 ± 0.0 | 2.9 | 10.4 |
| YOLO11s-seg | 640 | 46.6 | 37.8 | 117.6 ± 4.9 | 2.9 ± 0.0 | 10.1 | 35.5 |
| YOLO11m-seg | 640 | 51.5 | 41.5 | 281.6 ± 1.2 | 6.3 ± 0.1 | 22.4 | 123.3 |
| YOLO11l-seg | 640 | 53.4 | 42.9 | 344.2 ± 3.2 | 7.8 ± 0.2 | 27.6 | 142.2 |
| YOLO11x-seg | 640 | 54.7 | 43.8 | 664.5 ± 3.2 | 15.8 ± 0.7 | 62.1 | 319.0 |
yolo val segment data=coco.yaml device=0yolo val segment data=coco.yaml batch=1 device=0|cpuConsult the Classification Docs for usage examples. These models are trained on 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 |
|---|---|---|---|---|---|---|---|
| YOLO11n-cls | 224 | 70.0 | 89.4 | 5.0 ± 0.3 | 1.1 ± 0.0 | 1.6 | 0.5 |
| YOLO11s-cls | 224 | 75.4 | 92.7 | 7.9 ± 0.2 | 1.3 ± 0.0 | 5.5 | 1.6 |
| YOLO11m-cls | 224 | 77.3 | 93.9 | 17.2 ± 0.4 | 2.0 ± 0.0 | 10.4 | 5.0 |
| YOLO11l-cls | 224 | 78.3 | 94.3 | 23.2 ± 0.3 | 2.8 ± 0.0 | 12.9 | 6.2 |
| YOLO11x-cls | 224 | 79.5 | 94.9 | 41.4 ± 0.9 | 3.8 ± 0.0 | 28.4 | 13.7 |
yolo val classify data=path/to/ImageNet device=0yolo val classify data=path/to/ImageNet batch=1 device=0|cpuSee the Pose Estimation Docs for usage examples. These models are trained on COCO-Pose, focusing on the 'person' class.
| Model | size (pixels) | mAPpose 50-95 | mAPpose 50 | Speed CPU ONNX (ms) | Speed T4 TensorRT10 (ms) | params (M) | FLOPs (B) |
|---|---|---|---|---|---|---|---|
| YOLO11n-pose | 640 | 50.0 | 81.0 | 52.4 ± 0.5 | 1.7 ± 0.0 | 2.9 | 7.6 |
| YOLO11s-pose | 640 | 58.9 | 86.3 | 90.5 ± 0.6 | 2.6 ± 0.0 | 9.9 | 23.2 |
| YOLO11m-pose | 640 | 64.9 | 89.4 | 187.3 ± 0.8 | 4.9 ± 0.1 | 20.9 | 71.7 |
| YOLO11l-pose | 640 | 66.1 | 89.9 | 247.7 ± 1.1 | 6.4 ± 0.1 | 26.2 | 90.7 |
| YOLO11x-pose | 640 | 69.5 | 91.1 | 488.0 ± 13.9 | 12.1 ± 0.2 | 58.8 | 203.3 |
yolo val pose data=coco-pose.yaml device=0yolo val pose data=coco-pose.yaml batch=1 device=0|cpuCheck the OBB Docs for usage examples. These models are trained on DOTAv1, including 15 classes.
| Model | size (pixels) | mAPtest 50 | Speed CPU ONNX (ms) | Speed T4 TensorRT10 (ms) | params (M) | FLOPs (B) |
|---|---|---|---|---|---|---|
| YOLO11n-obb | 1024 | 78.4 | 117.6 ± 0.8 | 4.4 ± 0.0 | 2.7 | 17.2 |
| YOLO11s-obb | 1024 | 79.5 | 219.4 ± 4.0 | 5.1 ± 0.0 | 9.7 | 57.5 |
| YOLO11m-obb | 1024 | 80.9 | 562.8 ± 2.9 | 10.1 ± 0.4 | 20.9 | 183.5 |
| YOLO11l-obb | 1024 | 81.0 | 712.5 ± 5.0 | 13.5 ± 0.6 | 26.2 | 232.0 |
| YOLO11x-obb | 1024 | 81.3 | 1408.6 ± 7.7 | 28.6 ± 1.0 | 58.8 | 520.2 |
yolo val obb data=DOTAv1.yaml device=0 split=test and submit merged results to the DOTA evaluation server.yolo val obb data=DOTAv1.yaml batch=1 device=0|cpuOur 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, Comet ML, Roboflow, and Intel OpenVINO, can optimize your AI workflow. Explore more at Ultralytics Integrations.
| Ultralytics HUB 🌟 | Weights & Biases | Comet | Neural Magic |
|---|---|---|---|
| Streamline YOLO workflows: Label, train, and deploy effortlessly with Ultralytics HUB. Try now! | Track experiments, hyperparameters, and results with Weights & Biases. | Free forever, Comet ML lets you save YOLO models, resume training, and interactively visualize predictions. | Run YOLO inference up to 6x faster with Neural Magic DeepSparse. |
Experience seamless AI with Ultralytics HUB, the all-in-one platform for data visualization, training YOLO models, and deployment—no coding required. Transform images into actionable insights and bring your AI visions to life effortlessly using our cutting-edge platform and user-friendly Ultralytics App. Start your journey for Free today!
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 to get started. We also welcome your feedback—share your experience by completing our Survey. A huge Thank You 🙏 to everyone who contributes!
We look forward to your contributions to help make the Ultralytics ecosystem even better!
Ultralytics offers two licensing options to suit different needs:
For bug reports and feature requests related to Ultralytics software, please visit GitHub Issues. For questions, discussions, and community support, join our active communities on Discord, Reddit, and the Ultralytics Community Forums. We're here to help with all things Ultralytics!
FAQs
Automatically built Ultralytics package with python-opencv-headless dependency instead of python-opencv
We found that dgenerate-ultralytics-headless demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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