Ultralytics YOLO11 Launch 🚀

[glenn-jocher]

Ultralytics YOLO11

We are thrilled to announce the official launch of YOLO11, the latest iteration of the Ultralytics YOLO series, bringing unparalleled advancements in real-time object detection, segmentation, pose estimation, and classification. Building upon the success of YOLOv8, YOLO11 delivers state-of-the-art performance across the board with significant improvements in both speed and accuracy.

🚀 Key Performance Improvements:

• Accuracy Boost: YOLO11 achieves up to a 2% higher mAP (mean Average Precision) on COCO for object detection compared to YOLOv8.
• Efficiency & Speed: It boasts up to 22% fewer parameters than YOLOv8 models while improving real-time inference speeds by up to 2% faster, making it perfect for edge applications and resource-constrained environments.

📊 Quantitative Performance Comparison with YOLOv8:

Model	YOLOv8 mAPval (%)	YOLO11 mAPval (%)	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

Each variant of YOLO11 (n, s, m, l, x) is designed to offer the optimal balance of speed and accuracy, catering to diverse application needs.

🚀 Versatile Task Support

YOLO11 builds on the versatility of the YOLO series, handling diverse computer vision tasks seamlessly:

• Detection: Rapidly detect and localize objects within images or video frames.
• Instance Segmentation: Identify and segment objects at a pixel level for more granular insights.
• Pose Estimation: Detect key points for human pose estimation, suitable for fitness, sports analytics, and more.
• Oriented Object Detection (OBB): Detect objects with an orientation angle, perfect for aerial imagery and robotics.
• Classification: Classify whole images into categories, useful for tasks like product categorization.

📦 Quick Start Example

To get started with YOLO11, install the latest version of the Ultralytics package:

pip install ultralytics>=8.3.0

Then, load the 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()

With just a few lines of code, you can harness the power of YOLO11 for real-time object detection and other computer vision tasks.

🌐 Seamless Integration & Deployment

YOLO11 is designed for easy integration into existing workflows and is optimized for deployment across a variety of environments, from edge devices to cloud platforms, offering unmatched flexibility for diverse applications.

You can get started with YOLO11 today through the Ultralytics HUB and the Ultralytics Python package. Dive into the future of computer vision and experience how YOLO11 can power your AI projects! 🚀

[UltralyticsAssistant]

👋 Hello @glenn-jocher, thank you for sharing the exciting news about Ultralytics YOLO11 🚀! We're thrilled to see the advancements in real-time object detection, segmentation, and more.

If you have any 🐛 Bug Reports regarding YOLO11, please provide a minimum reproducible example to help us address it efficiently.

For custom training ❓ Questions or if you're starting fresh, the Docs are a great resource. You'll find detailed guidance on Tasks like detection, segmentation, pose estimation, and more to help you get the best results.

Join our community for real-time discussions on Discord 🎧, deeper conversations on Discourse, or check out our Subreddit.

Upgrade

Ensure you have the latest ultralytics package with all requirements for the best experience:

pip install -U ultralytics

Environments

YOLO11 can be explored in diverse environments. Try it in Colab or on Docker for seamless integration.

Status

Check our Ultralytics CI for the latest operational updates:

This is an automated response, and an Ultralytics engineer will assist further soon. Enjoy exploring YOLO11! 🎉

[13501714030]

can not export the v11 model to tflite, error message as below:

Ultralytics 8.3.2 🚀 Python-3.8.0 torch-2.0.1+cu117 CPU (12th Gen Intel Core(TM) i7-12700H)
YOLO11s summary (fused): 238 layers, 9,413,187 parameters, 0 gradients, 21.3 GFLOPs

PyTorch: starting from 'tk11s_150eps_lrf0.3_mxp_640x384.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 5, 8400) (18.3 MB)

TensorFlow SavedModel: starting export with tensorflow 2.13.0...

ONNX: starting export with onnx 1.16.2 opset 17...
============= Diagnostic Run torch.onnx.export version 2.0.1+cu117 =============
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================

ONNX: slimming with onnxslim 0.1.34...
ONNX: export success ✅ 2.4s, saved as 'tk11s_150eps_lrf0.3_mxp_640x384.onnx' (36.3 MB)
TensorFlow SavedModel: starting TFLite export with onnx2tf 1.20.0...
ERROR: The trace log is below.
Traceback (most recent call last):
File "C:\ProgramData\Miniconda3\envs\p4d_v11\lib\site-packages\onnx2tf\utils\common_functions.py", line 310, in print_wrapper_func
result = func(*args, **kwargs)
File "C:\ProgramData\Miniconda3\envs\p4d_v11\lib\site-packages\onnx2tf\utils\common_functions.py", line 383, in inverted_operation_enable_disable_wrapper_func
result = func(*args, **kwargs)
File "C:\ProgramData\Miniconda3\envs\p4d_v11\lib\site-packages\onnx2tf\utils\common_functions.py", line 53, in get_replacement_parameter_wrapper_func
func(*args, **kwargs)
File "C:\ProgramData\Miniconda3\envs\p4d_v11\lib\site-packages\onnx2tf\ops\Add.py", line 248, in make_node
merge_two_consecutive_identical_ops_into_one(
File "C:\ProgramData\Miniconda3\envs\p4d_v11\lib\site-packages\onnx2tf\utils\common_functions.py", line 5384, in merge_two_consecutive_identical_ops_into_one
tf.math.add(
File "C:\ProgramData\Miniconda3\envs\p4d_v11\lib\site-packages\tensorflow\python\util\traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\ProgramData\Miniconda3\envs\p4d_v11\lib\site-packages\keras\src\layers\core\tf_op_layer.py", line 119, in handle
return TFOpLambda(op)(*args, **kwargs)
File "C:\ProgramData\Miniconda3\envs\p4d_v11\lib\site-packages\keras\src\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
ValueError: Exception encountered when calling layer "tf.math.add_5" (type TFOpLambda).

Dimensions must be equal, but are 64 and 32 for '{{node tf.math.add_5/Add}} = AddV2[T=DT_FLOAT](Placeholder, Placeholder_1)' with input shapes: [1,80,160,64], [1,80,160,32].

Call arguments received by layer "tf.math.add_5" (type TFOpLambda):
• x=tf.Tensor(shape=(1, 80, 160, 64), dtype=float32)
• y=tf.Tensor(shape=(1, 80, 160, 32), dtype=float32)
• name='/model.2/m.0/Add'

ERROR: input_onnx_file_path: tk11s_150eps_lrf0.3_mxp_640x384.onnx
ERROR: onnx_op_name: /model.2/m.0/Add
ERROR: Read this and deal with it. https://github.com/PINTO0309/onnx2tf#parameter-replacement
ERROR: Alternatively, if the input OP has a dynamic dimension, use the -b or -ois option to rewrite it to a static shape and try again.
ERROR: If the input OP of ONNX before conversion is NHWC or an irregular channel arrangement other than NCHW, use the -kt or -kat option.
ERROR: Also, for models that include NonMaxSuppression in the post-processing, try the -onwdt option.

[matpalm]

looks like the trained model you're exporting is 640x640 :(

PyTorch: starting from 'tk11s_150eps_lrf0.3_mxp_640x384.pt' with input shape (1, 3, 640, 640) BCHW and 
output shape(s) (1, 5, 8400) (18.3 MB)

[13501714030]

yes, that's the problem, my command is: yolo export model=XXXX format=tflite imgsz=384,640, i have input the image size is 640x384, why the exporting progam generated the image size is 640 x 640? the export command is changed? i use this command on YOLOV8, it works very well. :(

[glenn-jocher]

@13501714030 your command looks correct for the specified image size. The error might be due to a dimension mismatch in the model layers. Try checking the model architecture or simplifying it before export.

[Chirag-v09]

is it resolved?

[glenn-jocher]

If you're still experiencing issues, please ensure the model's input dimensions match your export settings. Let me know if you need further assistance!

[luckyboby12]

Hello ! I recently considered loading a tri-modal data set based on the yolov8 algorithm framework, including visible and infrared data sets, and a text data set. I don 't know how to load data, can you give me some advice

[glenn-jocher]

@luckyboby12 thank you for your question! YOLO models, including YOLOv8, support custom datasets in the YOLO format. For tri-modal datasets (e.g., visible, infrared, and text), you'll need to preprocess and align your data into a compatible structure, likely combining the modalities through feature fusion or custom data loaders. You can refer to the Ultralytics dataset documentation for guidance on formatting and structuring your dataset. For additional flexibility, consider modifying the YOLO data pipeline to handle your specific modalities. Let me know if you need further clarification!