Just tested the Ultralytics YOLO11 models, performing well!
I experimented with various tasks like detection, segmentation, prediction, and pose estimation using random images, and the results look fantastic!
In case you want to give it a try yolo predict source='path/to/image.png
Learn more YOLO11 - Ultralytics YOLO Docs
Object Detection |
Pose Estimation |
Object Detection |
Image Segmentation |
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More details here!
R&D Highlights
- 25 Open-Source Models: YOLO11 introduces 25 models across 5 sizes and 5 tasks, ensuring thereβs an optimized model for any use case.
- Accuracy Boost: YOLO11n achieves up to a 2.2% higher mAP (37.3 β 39.5) on COCO object detection tasks compared to YOLOv8n.
- Efficiency & Speed: YOLO11 uses up to 22% fewer parameters than YOLOv8 and provides up to 2% faster inference speeds. Optimized for edge applications and resource-constrained environments.
The focus of YOLO11 is on refining architecture to improve performance while reducing computational requirementsβa great fit for those who need both precision and speed.
YOLO11 Benchmarks
The improvements are consistent across all model sizes, providing a noticeable upgrade for current YOLO users.
Model |
YOLOv8 mAP (%) |
YOLO11 mAP (%) |
YOLOv8 Params (M) |
YOLO11 Params (M) |
Improvement |
YOLOn |
37.3 |
39.5 |
3.2 |
2.6 |
+2.2% mAP |
YOLOs |
44.9 |
47.0 |
11.2 |
9.4 |
+2.1% mAP |
YOLOm |
50.2 |
51.5 |
25.9 |
20.1 |
+1.3% mAP |
YOLOl |
52.9 |
53.4 |
43.7 |
25.3 |
+0.5% mAP |
YOLOx |
53.9 |
54.7 |
68.2 |
56.9 |
+0.8% mAP |
Versatile Task Support
YOLO11 extends the capabilities of the YOLO series to cover multiple computer vision tasks:
Quick Start Example
If youβre already using the Ultralytics package, upgrading to YOLO11 is easy. Install the latest package:
pip install ultralytics>=8.3.0
Then, load a pre-trained YOLO11 model and run inference on an image:
from ultralytics import YOLO
# Load the YOLO11 model
model = YOLO("yolo11n.pt")
# Run inference on an image
results = model("path/to/image.jpg")
# Display results
results[0].show()
These few lines of code are all you need to start using YOLO11 for your real-time computer vision needs.
Access and Get Involved
YOLO11 is open-source and designed to integrate smoothly into various workflows, from edge devices to cloud platforms. You can explore the models and contribute at GitHub - ultralytics/ultralytics: NEW - YOLOv8 π in PyTorch > ONNX > OpenVINO > CoreML > TFLite.
Check it out, see how it fits into your projects, and let us know your feedback!
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