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docs/en/reference/utils/checks.md 0 → 100644
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---
description: Explore utility functions for Ultralytics YOLO such as checking versions, image sizes, and requirements.
keywords: Ultralytics, YOLO, utility functions, version checks, requirements, image size
---
# Reference for `ultralytics/utils/checks.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/checks.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/checks.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/checks.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.checks.parse_requirements
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## ::: ultralytics.utils.checks.parse_version
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## ::: ultralytics.utils.checks.is_ascii
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## ::: ultralytics.utils.checks.check_imgsz
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## ::: ultralytics.utils.checks.check_version
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## ::: ultralytics.utils.checks.check_latest_pypi_version
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## ::: ultralytics.utils.checks.check_pip_update_available
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## ::: ultralytics.utils.checks.check_font
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## ::: ultralytics.utils.checks.check_python
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## ::: ultralytics.utils.checks.check_requirements
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## ::: ultralytics.utils.checks.check_torchvision
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## ::: ultralytics.utils.checks.check_suffix
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## ::: ultralytics.utils.checks.check_yolov5u_filename
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## ::: ultralytics.utils.checks.check_model_file_from_stem
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## ::: ultralytics.utils.checks.check_file
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## ::: ultralytics.utils.checks.check_yaml
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## ::: ultralytics.utils.checks.check_is_path_safe
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## ::: ultralytics.utils.checks.check_imshow
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## ::: ultralytics.utils.checks.check_yolo
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## ::: ultralytics.utils.checks.collect_system_info
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## ::: ultralytics.utils.checks.check_amp
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## ::: ultralytics.utils.checks.git_describe
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## ::: ultralytics.utils.checks.print_args
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## ::: ultralytics.utils.checks.cuda_device_count
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## ::: ultralytics.utils.checks.cuda_is_available
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docs/en/reference/utils/dist.md 0 → 100644
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---
description: Explore Ultralytics' utilities for distributed training including DDP file generation, command setup, and cleanup. Improve multi-node training efficiency.
keywords: Ultralytics, distributed training, DDP, multi-node training, network port, DDP file generation, DDP command, training utilities
---
# Reference for `ultralytics/utils/dist.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/dist.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/dist.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/dist.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.dist.find_free_network_port
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## ::: ultralytics.utils.dist.generate_ddp_file
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## ::: ultralytics.utils.dist.generate_ddp_command
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## ::: ultralytics.utils.dist.ddp_cleanup
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docs/en/reference/utils/downloads.md 0 → 100644
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---
description: Explore and utilize the Ultralytics download utilities to handle URLs, zip/unzip files, and manage GitHub assets effectively.
keywords: Ultralytics, download utilities, URL validation, zip directory, unzip file, check disk space, Google Drive, GitHub assets, YOLO, machine learning
---
# Reference for `ultralytics/utils/downloads.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/downloads.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/downloads.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/downloads.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.downloads.is_url
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## ::: ultralytics.utils.downloads.delete_dsstore
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## ::: ultralytics.utils.downloads.zip_directory
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## ::: ultralytics.utils.downloads.unzip_file
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## ::: ultralytics.utils.downloads.check_disk_space
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## ::: ultralytics.utils.downloads.get_google_drive_file_info
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## ::: ultralytics.utils.downloads.safe_download
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## ::: ultralytics.utils.downloads.get_github_assets
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## ::: ultralytics.utils.downloads.attempt_download_asset
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## ::: ultralytics.utils.downloads.download
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docs/en/reference/utils/errors.md 0 → 100644
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---
description: Explore error handling for Ultralytics YOLO. Learn about custom exceptions like HUBModelError to manage model fetching issues effectively.
keywords: Ultralytics, YOLO, error handling, HUBModelError, model fetching, custom exceptions, Python
---
# Reference for `ultralytics/utils/errors.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/errors.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/errors.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/errors.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.errors.HUBModelError
<br><br>
docs/en/reference/utils/files.md 0 → 100644
View file @ af155c51
---
description: Explore the utility functions and context managers in Ultralytics like WorkingDirectory, increment_path, file_size, and more. Enhance your file handling in Python.
keywords: Ultralytics, file utilities, Python, WorkingDirectory, increment_path, file_size, file_age, contexts, file handling, file management
---
# Reference for `ultralytics/utils/files.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/files.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/files.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/files.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.files.WorkingDirectory
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## ::: ultralytics.utils.files.spaces_in_path
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## ::: ultralytics.utils.files.increment_path
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## ::: ultralytics.utils.files.file_age
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## ::: ultralytics.utils.files.file_date
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## ::: ultralytics.utils.files.file_size
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## ::: ultralytics.utils.files.get_latest_run
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## ::: ultralytics.utils.files.update_models
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docs/en/reference/utils/instance.md 0 → 100644
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---
description: Explore Ultralytics utilities for bounding boxes and instances, providing detailed documentation on handling bbox formats, conversions, and more.
keywords: Ultralytics, bounding boxes, Instances, bbox formats, conversions, AI, deep learning, YOLO, xyxy, xywh, ltwh
---
# Reference for `ultralytics/utils/instance.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/instance.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/instance.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/instance.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.instance.Bboxes
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## ::: ultralytics.utils.instance.Instances
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## ::: ultralytics.utils.instance._ntuple
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docs/en/reference/utils/loss.md 0 → 100644
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---
description: Explore detailed descriptions and implementations of various loss functions used in Ultralytics models, including Varifocal Loss, Focal Loss, Bbox Loss, and more.
keywords: Ultralytics, loss functions, Varifocal Loss, Focal Loss, Bbox Loss, Rotated Bbox Loss, Keypoint Loss, YOLO, model training, documentation
---
# Reference for `ultralytics/utils/loss.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/loss.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/loss.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/loss.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.loss.VarifocalLoss
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## ::: ultralytics.utils.loss.FocalLoss
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## ::: ultralytics.utils.loss.DFLoss
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## ::: ultralytics.utils.loss.BboxLoss
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## ::: ultralytics.utils.loss.RotatedBboxLoss
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## ::: ultralytics.utils.loss.KeypointLoss
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## ::: ultralytics.utils.loss.v8DetectionLoss
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## ::: ultralytics.utils.loss.v8SegmentationLoss
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## ::: ultralytics.utils.loss.v8PoseLoss
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## ::: ultralytics.utils.loss.v8ClassificationLoss
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## ::: ultralytics.utils.loss.v8OBBLoss
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## ::: ultralytics.utils.loss.E2EDetectLoss
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docs/en/reference/utils/metrics.md 0 → 100644
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---
description: Explore detailed metrics and utility functions for model validation and performance analysis with Ultralytics' metrics module.
keywords: Ultralytics, metrics, model validation, performance analysis, IoU, confusion matrix
---
# Reference for `ultralytics/utils/metrics.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/metrics.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/metrics.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/metrics.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.metrics.ConfusionMatrix
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## ::: ultralytics.utils.metrics.Metric
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## ::: ultralytics.utils.metrics.DetMetrics
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## ::: ultralytics.utils.metrics.SegmentMetrics
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## ::: ultralytics.utils.metrics.PoseMetrics
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## ::: ultralytics.utils.metrics.ClassifyMetrics
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## ::: ultralytics.utils.metrics.OBBMetrics
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## ::: ultralytics.utils.metrics.bbox_ioa
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## ::: ultralytics.utils.metrics.box_iou
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## ::: ultralytics.utils.metrics.bbox_iou
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## ::: ultralytics.utils.metrics.mask_iou
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## ::: ultralytics.utils.metrics.kpt_iou
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## ::: ultralytics.utils.metrics._get_covariance_matrix
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## ::: ultralytics.utils.metrics.probiou
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## ::: ultralytics.utils.metrics.batch_probiou
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## ::: ultralytics.utils.metrics.smooth_BCE
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## ::: ultralytics.utils.metrics.smooth
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## ::: ultralytics.utils.metrics.plot_pr_curve
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## ::: ultralytics.utils.metrics.plot_mc_curve
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## ::: ultralytics.utils.metrics.compute_ap
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## ::: ultralytics.utils.metrics.ap_per_class
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docs/en/reference/utils/ops.md 0 → 100644
View file @ af155c51
---
description: Explore detailed documentation on utility operations in Ultralytics including non-max suppression, bounding box transformations, and more.
keywords: Ultralytics, utility operations, non-max suppression, bounding box transformations, YOLOv8, machine learning
---
# Reference for `ultralytics/utils/ops.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/ops.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.ops.Profile
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## ::: ultralytics.utils.ops.segment2box
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## ::: ultralytics.utils.ops.scale_boxes
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## ::: ultralytics.utils.ops.make_divisible
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## ::: ultralytics.utils.ops.nms_rotated
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## ::: ultralytics.utils.ops.non_max_suppression
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## ::: ultralytics.utils.ops.clip_boxes
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## ::: ultralytics.utils.ops.clip_coords
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## ::: ultralytics.utils.ops.scale_image
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## ::: ultralytics.utils.ops.xyxy2xywh
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## ::: ultralytics.utils.ops.xywh2xyxy
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## ::: ultralytics.utils.ops.xywhn2xyxy
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## ::: ultralytics.utils.ops.xyxy2xywhn
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## ::: ultralytics.utils.ops.xywh2ltwh
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## ::: ultralytics.utils.ops.xyxy2ltwh
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## ::: ultralytics.utils.ops.ltwh2xywh
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## ::: ultralytics.utils.ops.xyxyxyxy2xywhr
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## ::: ultralytics.utils.ops.xywhr2xyxyxyxy
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## ::: ultralytics.utils.ops.ltwh2xyxy
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## ::: ultralytics.utils.ops.segments2boxes
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## ::: ultralytics.utils.ops.resample_segments
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## ::: ultralytics.utils.ops.crop_mask
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## ::: ultralytics.utils.ops.process_mask
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## ::: ultralytics.utils.ops.process_mask_native
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## ::: ultralytics.utils.ops.scale_masks
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## ::: ultralytics.utils.ops.scale_coords
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## ::: ultralytics.utils.ops.regularize_rboxes
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## ::: ultralytics.utils.ops.masks2segments
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## ::: ultralytics.utils.ops.convert_torch2numpy_batch
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## ::: ultralytics.utils.ops.clean_str
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docs/en/reference/utils/patches.md 0 → 100644
View file @ af155c51
---
description: Explore and contribute to Ultralytics' utils/patches.py. Learn about the imread, imwrite, imshow, and torch_save functions.
keywords: Ultralytics, utils, patches, imread, imwrite, imshow, torch_save, OpenCV, PyTorch, GitHub
---
# Reference for `ultralytics/utils/patches.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/patches.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/patches.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/patches.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.patches.imread
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## ::: ultralytics.utils.patches.imwrite
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## ::: ultralytics.utils.patches.imshow
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## ::: ultralytics.utils.patches.torch_load
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## ::: ultralytics.utils.patches.torch_save
<br><br>
docs/en/reference/utils/plotting.md 0 → 100644
View file @ af155c51
---
description: Explore detailed functionalities of Ultralytics plotting utilities for data visualizations and custom annotations in ML projects.
keywords: ultralytics, plotting, utilities, documentation, data visualization, annotations, python, ML tools
---
# Reference for `ultralytics/utils/plotting.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/plotting.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/plotting.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/plotting.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.plotting.Colors
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## ::: ultralytics.utils.plotting.Annotator
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## ::: ultralytics.utils.plotting.plot_labels
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## ::: ultralytics.utils.plotting.save_one_box
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## ::: ultralytics.utils.plotting.plot_images
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## ::: ultralytics.utils.plotting.plot_results
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## ::: ultralytics.utils.plotting.plt_color_scatter
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## ::: ultralytics.utils.plotting.plot_tune_results
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## ::: ultralytics.utils.plotting.output_to_target
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## ::: ultralytics.utils.plotting.output_to_rotated_target
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## ::: ultralytics.utils.plotting.feature_visualization
<br><br>
docs/en/reference/utils/tal.md 0 → 100644
View file @ af155c51
---
description: Explore the TaskAlignedAssigner in Ultralytics YOLO. Learn about the TaskAlignedMetric and its applications in object detection.
keywords: Ultralytics, YOLO, TaskAlignedAssigner, object detection, machine learning, AI, Tal.py, PyTorch
---
# Reference for `ultralytics/utils/tal.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/tal.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/tal.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/tal.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.tal.TaskAlignedAssigner
<br><br><hr><br>
## ::: ultralytics.utils.tal.RotatedTaskAlignedAssigner
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## ::: ultralytics.utils.tal.make_anchors
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## ::: ultralytics.utils.tal.dist2bbox
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## ::: ultralytics.utils.tal.bbox2dist
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## ::: ultralytics.utils.tal.dist2rbox
<br><br>
docs/en/reference/utils/torch_utils.md 0 → 100644
View file @ af155c51
---
description: Explore valuable torch utilities from Ultralytics for optimized model performance, including device selection, model fusion, and inference optimization.
keywords: Ultralytics, torch utils, model optimization, device selection, inference optimization, model fusion, CPU info, PyTorch tools
---
# Reference for `ultralytics/utils/torch_utils.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/torch_utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/torch_utils.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/torch_utils.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.torch_utils.ModelEMA
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## ::: ultralytics.utils.torch_utils.EarlyStopping
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## ::: ultralytics.utils.torch_utils.FXModel
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## ::: ultralytics.utils.torch_utils.torch_distributed_zero_first
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## ::: ultralytics.utils.torch_utils.smart_inference_mode
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## ::: ultralytics.utils.torch_utils.autocast
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## ::: ultralytics.utils.torch_utils.get_cpu_info
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## ::: ultralytics.utils.torch_utils.get_gpu_info
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## ::: ultralytics.utils.torch_utils.select_device
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## ::: ultralytics.utils.torch_utils.time_sync
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## ::: ultralytics.utils.torch_utils.fuse_conv_and_bn
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## ::: ultralytics.utils.torch_utils.fuse_deconv_and_bn
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## ::: ultralytics.utils.torch_utils.model_info
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## ::: ultralytics.utils.torch_utils.get_num_params
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## ::: ultralytics.utils.torch_utils.get_num_gradients
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## ::: ultralytics.utils.torch_utils.model_info_for_loggers
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## ::: ultralytics.utils.torch_utils.get_flops
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## ::: ultralytics.utils.torch_utils.get_flops_with_torch_profiler
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## ::: ultralytics.utils.torch_utils.initialize_weights
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## ::: ultralytics.utils.torch_utils.scale_img
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## ::: ultralytics.utils.torch_utils.copy_attr
<br><br><hr><br>
## ::: ultralytics.utils.torch_utils.get_latest_opset
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## ::: ultralytics.utils.torch_utils.intersect_dicts
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## ::: ultralytics.utils.torch_utils.is_parallel
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## ::: ultralytics.utils.torch_utils.de_parallel
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## ::: ultralytics.utils.torch_utils.one_cycle
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## ::: ultralytics.utils.torch_utils.init_seeds
<br><br><hr><br>
## ::: ultralytics.utils.torch_utils.strip_optimizer
<br><br><hr><br>
## ::: ultralytics.utils.torch_utils.convert_optimizer_state_dict_to_fp16
<br><br><hr><br>
## ::: ultralytics.utils.torch_utils.profile
<br><br>
docs/en/reference/utils/triton.md 0 → 100644
View file @ af155c51
---
description: Learn how to use the TritonRemoteModel class for interacting with remote Triton Inference Server models. Detailed guide with code examples and attributes.
keywords: Ultralytics, TritonRemoteModel, Triton Inference Server, model client, inference, remote model, machine learning, AI, Python
---
# Reference for `ultralytics/utils/triton.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/triton.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/triton.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/triton.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.triton.TritonRemoteModel
<br><br>
docs/en/reference/utils/tuner.md 0 → 100644
View file @ af155c51
---
description: Explore how to use ultralytics.utils.tuner.py for efficient hyperparameter tuning with Ray Tune. Learn implementation details and example usage.
keywords: Ultralytics, tuner, hyperparameter tuning, Ray Tune, YOLO, machine learning, AI, optimization
---
# Reference for `ultralytics/utils/tuner.py`
!!! note
This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/tuner.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/tuner.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/utils/tuner.py) 🛠️. Thank you 🙏!
<br>
## ::: ultralytics.utils.tuner.run_ray_tune
<br><br>
docs/en/robots.txt 0 → 100644
View file @ af155c51
User-agent: *
Sitemap: https://docs.ultralytics.com/sitemap.xml
Sitemap: https://docs.ultralytics.com/ar/sitemap.xml
Sitemap: https://docs.ultralytics.com/de/sitemap.xml
Sitemap: https://docs.ultralytics.com/es/sitemap.xml
Sitemap: https://docs.ultralytics.com/fr/sitemap.xml
Sitemap: https://docs.ultralytics.com/it/sitemap.xml
Sitemap: https://docs.ultralytics.com/ja/sitemap.xml
Sitemap: https://docs.ultralytics.com/ko/sitemap.xml
Sitemap: https://docs.ultralytics.com/pt/sitemap.xml
Sitemap: https://docs.ultralytics.com/ru/sitemap.xml
Sitemap: https://docs.ultralytics.com/tr/sitemap.xml
Sitemap: https://docs.ultralytics.com/vi/sitemap.xml
Sitemap: https://docs.ultralytics.com/zh/sitemap.xml
docs/en/solutions/index.md 0 → 100644
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---
comments: true
description: Explore Ultralytics Solutions using YOLO11 for object counting, blurring, security, and more. Enhance efficiency and solve real-world problems with cutting-edge AI.
keywords: Ultralytics, YOLO11, object counting, object blurring, security systems, AI solutions, real-time analysis, computer vision applications
---
# Ultralytics Solutions: Harness YOLO11 to Solve Real-World Problems
Ultralytics Solutions provide cutting-edge applications of YOLO models, offering real-world solutions like object counting, blurring, and security systems, enhancing efficiency and [accuracy](https://www.ultralytics.com/glossary/accuracy) in diverse industries. Discover the power of YOLO11 for practical, impactful implementations.
![Ultralytics Solutions Thumbnail](https://github.com/ultralytics/docs/releases/download/0/ultralytics-solutions-thumbnail.avif)
## Solutions
Here's our curated list of Ultralytics solutions that can be used to create awesome [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) projects.
- [Object Counting](../guides/object-counting.md) 🚀: Learn to perform real-time object counting with YOLO11. Gain the expertise to accurately count objects in live video streams.
- [Object Cropping](../guides/object-cropping.md) 🚀: Master object cropping with YOLO11 for precise extraction of objects from images and videos.
- [Object Blurring](../guides/object-blurring.md) 🚀: Apply object blurring using YOLO11 to protect privacy in image and video processing.
- [Workouts Monitoring](../guides/workouts-monitoring.md) 🚀: Discover how to monitor workouts using YOLO11. Learn to track and analyze various fitness routines in real time.
- [Objects Counting in Regions](../guides/region-counting.md) 🚀: Count objects in specific regions using YOLO11 for accurate detection in varied areas.
- [Security Alarm System](../guides/security-alarm-system.md) 🚀: Create a security alarm system with YOLO11 that triggers alerts upon detecting new objects. Customize the system to fit your specific needs.
- [Heatmaps](../guides/heatmaps.md) 🚀: Utilize detection heatmaps to visualize data intensity across a matrix, providing clear insights in computer vision tasks.
- [Instance Segmentation with Object Tracking](../guides/instance-segmentation-and-tracking.md) 🚀 NEW: Implement [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation) and object tracking with YOLO11 to achieve precise object boundaries and continuous monitoring.
- [VisionEye View Objects Mapping](../guides/vision-eye.md) 🚀: Develop systems that mimic human eye focus on specific objects, enhancing the computer's ability to discern and prioritize details.
- [Speed Estimation](../guides/speed-estimation.md) 🚀: Estimate object speed using YOLO11 and object tracking techniques, crucial for applications like autonomous vehicles and traffic monitoring.
- [Distance Calculation](../guides/distance-calculation.md) 🚀: Calculate distances between objects using [bounding box](https://www.ultralytics.com/glossary/bounding-box) centroids in YOLO11, essential for spatial analysis.
- [Queue Management](../guides/queue-management.md) 🚀: Implement efficient queue management systems to minimize wait times and improve productivity using YOLO11.
- [Parking Management](../guides/parking-management.md) 🚀: Organize and direct vehicle flow in parking areas with YOLO11, optimizing space utilization and user experience.
- [Analytics](../guides/analytics.md) 📊 NEW: Conduct comprehensive data analysis to discover patterns and make informed decisions, leveraging YOLO11 for descriptive, predictive, and prescriptive analytics.
- [Live Inference with Streamlit](../guides/streamlit-live-inference.md) 🚀: Leverage the power of YOLO11 for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) directly through your web browser with a user-friendly Streamlit interface.
## Solutions Usage
!!! tip "Command Info"
`yolo SOLUTIONS SOLUTION_NAME ARGS`
- **SOLUTIONS** is a required keyword.
- **SOLUTION_NAME** (optional) is one of: `['count', 'heatmap', 'queue', 'speed', 'workout', 'analytics']`.
- **ARGS** (optional) are custom `arg=value` pairs, such as `show_in=True`, to override default settings.
=== "CLI"
```bash
yolo solutions count show=True # for object counting
yolo solutions source="path/to/video/file.mp4" # specify video file path
```
## Contribute to Our Solutions
We welcome contributions from the community! If you've mastered a particular aspect of Ultralytics YOLO that's not yet covered in our solutions, we encourage you to share your expertise. Writing a guide is a great way to give back to the community and help us make our documentation more comprehensive and user-friendly.
To get started, please read our [Contributing Guide](../help/contributing.md) for guidelines on how to open up a Pull Request (PR) 🛠️. We look forward to your contributions!
Let's work together to make the Ultralytics YOLO ecosystem more robust and versatile 🙏!
## FAQ
### How can I use Ultralytics YOLO for real-time object counting?
Ultralytics YOLO11 can be used for real-time object counting by leveraging its advanced object detection capabilities. You can follow our detailed guide on [Object Counting](../guides/object-counting.md) to set up YOLO11 for live video stream analysis. Simply install YOLO11, load your model, and process video frames to count objects dynamically.
### What are the benefits of using Ultralytics YOLO for security systems?
Ultralytics YOLO11 enhances security systems by offering real-time object detection and alert mechanisms. By employing YOLO11, you can create a security alarm system that triggers alerts when new objects are detected in the surveillance area. Learn how to set up a [Security Alarm System](../guides/security-alarm-system.md) with YOLO11 for robust security monitoring.
### How can Ultralytics YOLO improve queue management systems?
Ultralytics YOLO11 can significantly improve queue management systems by accurately counting and tracking people in queues, thus helping to reduce wait times and optimize service efficiency. Follow our detailed guide on [Queue Management](../guides/queue-management.md) to learn how to implement YOLO11 for effective queue monitoring and analysis.
### Can Ultralytics YOLO be used for workout monitoring?
Yes, Ultralytics YOLO11 can be effectively used for monitoring workouts by tracking and analyzing fitness routines in real-time. This allows for precise evaluation of exercise form and performance. Explore our guide on [Workouts Monitoring](../guides/workouts-monitoring.md) to learn how to set up an AI-powered workout monitoring system using YOLO11.
### How does Ultralytics YOLO help in creating heatmaps for [data visualization](https://www.ultralytics.com/glossary/data-visualization)?
Ultralytics YOLO11 can generate heatmaps to visualize data intensity across a given area, highlighting regions of high activity or interest. This feature is particularly useful in understanding patterns and trends in various computer vision tasks. Learn more about creating and using [Heatmaps](../guides/heatmaps.md) with YOLO11 for comprehensive data analysis and visualization.
docs/en/tasks/classify.md 0 → 100644
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---
comments: true
description: Master image classification using YOLO11. Learn to train, validate, predict, and export models efficiently.
keywords: YOLO11, image classification, AI, machine learning, pretrained models, ImageNet, model export, predict, train, validate
model_name: yolo11n-cls
---
# Image Classification
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/image-classification-examples.avif" alt="Image classification examples">
[Image classification](https://www.ultralytics.com/glossary/image-classification) is the simplest of the three tasks and involves classifying an entire image into one of a set of predefined classes.
The output of an image classifier is a single class label and a confidence score. Image classification is useful when you need to know only what class an image belongs to and don't need to know where objects of that class are located or what their exact shape is.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/5BO0Il_YYAg"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Explore Ultralytics YOLO Tasks: Image Classification using Ultralytics HUB
</p>
!!! tip
YOLO11 Classify models use the `-cls` suffix, i.e. `yolo11n-cls.pt` and are pretrained on [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml).
## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/11)
YOLO11 pretrained Classify models are shown here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset.
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
{% include "macros/yolo-cls-perf.md" %}
- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set. <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
## Train
Train YOLO11n-cls on the MNIST160 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) at image size 64. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n-cls.yaml") # build a new model from YAML
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n-cls.yaml").load("yolo11n-cls.pt") # build from YAML and transfer weights
# Train the model
results = model.train(data="mnist160", epochs=100, imgsz=64)
```
=== "CLI"
```bash
# Build a new model from YAML and start training from scratch
yolo classify train data=mnist160 model=yolo11n-cls.yaml epochs=100 imgsz=64
# Start training from a pretrained *.pt model
yolo classify train data=mnist160 model=yolo11n-cls.pt epochs=100 imgsz=64
# Build a new model from YAML, transfer pretrained weights to it and start training
yolo classify train data=mnist160 model=yolo11n-cls.yaml pretrained=yolo11n-cls.pt epochs=100 imgsz=64
```
### Dataset format
YOLO classification dataset format can be found in detail in the [Dataset Guide](../datasets/classify/index.md).
## Val
Validate trained YOLO11n-cls model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the MNIST160 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n-cls.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.top1 # top1 accuracy
metrics.top5 # top5 accuracy
```
=== "CLI"
```bash
yolo classify val model=yolo11n-cls.pt # val official model
yolo classify val model=path/to/best.pt # val custom model
```
## Predict
Use a trained YOLO11n-cls model to run predictions on images.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n-cls.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Predict with the model
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
```
=== "CLI"
```bash
yolo classify predict model=yolo11n-cls.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
yolo classify predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
```
See full `predict` mode details in the [Predict](../modes/predict.md) page.
## Export
Export a YOLO11n-cls model to a different format like ONNX, CoreML, etc.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n-cls.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained model
# Export the model
model.export(format="onnx")
```
=== "CLI"
```bash
yolo export model=yolo11n-cls.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
Available YOLO11-cls export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolo11n-cls.onnx`. Usage examples are shown for your model after export completes.
{% include "macros/export-table.md" %}
See full `export` details in the [Export](../modes/export.md) page.
## FAQ
### What is the purpose of YOLO11 in image classification?
YOLO11 models, such as `yolo11n-cls.pt`, are designed for efficient image classification. They assign a single class label to an entire image along with a confidence score. This is particularly useful for applications where knowing the specific class of an image is sufficient, rather than identifying the location or shape of objects within the image.
### How do I train a YOLO11 model for image classification?
To train a YOLO11 model, you can use either Python or CLI commands. For example, to train a `yolo11n-cls` model on the MNIST160 dataset for 100 epochs at an image size of 64:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n-cls.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="mnist160", epochs=100, imgsz=64)
```
=== "CLI"
```bash
yolo classify train data=mnist160 model=yolo11n-cls.pt epochs=100 imgsz=64
```
For more configuration options, visit the [Configuration](../usage/cfg.md) page.
### Where can I find pretrained YOLO11 classification models?
Pretrained YOLO11 classification models can be found in the [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/11) section. Models like `yolo11n-cls.pt`, `yolo11s-cls.pt`, `yolo11m-cls.pt`, etc., are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset and can be easily downloaded and used for various image classification tasks.
### How can I export a trained YOLO11 model to different formats?
You can export a trained YOLO11 model to various formats using Python or CLI commands. For instance, to export a model to ONNX format:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n-cls.pt") # load the trained model
# Export the model to ONNX
model.export(format="onnx")
```
=== "CLI"
```bash
yolo export model=yolo11n-cls.pt format=onnx # export the trained model to ONNX format
```
For detailed export options, refer to the [Export](../modes/export.md) page.
### How do I validate a trained YOLO11 classification model?
To validate a trained model's accuracy on a dataset like MNIST160, you can use the following Python or CLI commands:
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n-cls.pt") # load the trained model
# Validate the model
metrics = model.val() # no arguments needed, uses the dataset and settings from training
metrics.top1 # top1 accuracy
metrics.top5 # top5 accuracy
```
=== "CLI"
```bash
yolo classify val model=yolo11n-cls.pt # validate the trained model
```
For more information, visit the [Validate](#val) section.
docs/en/tasks/detect.md 0 → 100644
View file @ af155c51
---
comments: true
description: Learn about object detection with YOLO11. Explore pretrained models, training, validation, prediction, and export details for efficient object recognition.
keywords: object detection, YOLO11, pretrained models, training, validation, prediction, export, machine learning, computer vision
---
# Object Detection
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/object-detection-examples.avif" alt="Object detection examples">
[Object detection](https://www.ultralytics.com/glossary/object-detection) is a task that involves identifying the location and class of objects in an image or video stream.
The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with class labels and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a scene, but don't need to know exactly where the object is or its exact shape.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/5ku7npMrW40?si=6HQO1dDXunV8gekh"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Object Detection with Pre-trained Ultralytics YOLO Model.
</p>
!!! tip
YOLO11 Detect models are the default YOLO11 models, i.e. `yolo11n.pt` and are pretrained on [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml).
## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/11)
YOLO11 pretrained Detect models are shown here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset.
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
{% include "macros/yolo-det-perf.md" %}
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org/) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco.yaml batch=1 device=0|cpu`
## Train
Train YOLO11n on the COCO8 dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) at image size 640. For a full list of available arguments see the [Configuration](../usage/cfg.md) page.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.yaml") # build a new model from YAML
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
model = YOLO("yolo11n.yaml").load("yolo11n.pt") # build from YAML and transfer weights
# Train the model
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
# Build a new model from YAML and start training from scratch
yolo detect train data=coco8.yaml model=yolo11n.yaml epochs=100 imgsz=640
# Start training from a pretrained *.pt model
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
# Build a new model from YAML, transfer pretrained weights to it and start training
yolo detect train data=coco8.yaml model=yolo11n.yaml pretrained=yolo11n.pt epochs=100 imgsz=640
```
### Dataset format
YOLO detection dataset format can be found in detail in the [Dataset Guide](../datasets/detect/index.md). To convert your existing dataset from other formats (like COCO etc.) to YOLO format, please use [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) tool by Ultralytics.
## Val
Validate trained YOLO11n model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
metrics.box.map # map50-95
metrics.box.map50 # map50
metrics.box.map75 # map75
metrics.box.maps # a list contains map50-95 of each category
```
=== "CLI"
```bash
yolo detect val model=yolo11n.pt # val official model
yolo detect val model=path/to/best.pt # val custom model
```
## Predict
Use a trained YOLO11n model to run predictions on images.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Predict with the model
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
```
=== "CLI"
```bash
yolo detect predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
```
See full `predict` mode details in the [Predict](../modes/predict.md) page.
## Export
Export a YOLO11n model to a different format like ONNX, CoreML, etc.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained model
# Export the model
model.export(format="onnx")
```
=== "CLI"
```bash
yolo export model=yolo11n.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
Available YOLO11 export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolo11n.onnx`. Usage examples are shown for your model after export completes.
{% include "macros/export-table.md" %}
See full `export` details in the [Export](../modes/export.md) page.
## FAQ
### How do I train a YOLO11 model on my custom dataset?
Training a YOLO11 model on a custom dataset involves a few steps:
1. **Prepare the Dataset**: Ensure your dataset is in the YOLO format. For guidance, refer to our [Dataset Guide](../datasets/detect/index.md).
2. **Load the Model**: Use the Ultralytics YOLO library to load a pre-trained model or create a new model from a YAML file.
3. **Train the Model**: Execute the `train` method in Python or the `yolo detect train` command in CLI.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a pretrained model
model = YOLO("yolo11n.pt")
# Train the model on your custom dataset
model.train(data="my_custom_dataset.yaml", epochs=100, imgsz=640)
```
=== "CLI"
```bash
yolo detect train data=my_custom_dataset.yaml model=yolo11n.pt epochs=100 imgsz=640
```
For detailed configuration options, visit the [Configuration](../usage/cfg.md) page.
### What pretrained models are available in YOLO11?
Ultralytics YOLO11 offers various pretrained models for object detection, segmentation, and pose estimation. These models are pretrained on the COCO dataset or ImageNet for classification tasks. Here are some of the available models:
- [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt)
- [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt)
- [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt)
- [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt)
- [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt)
For a detailed list and performance metrics, refer to the [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/11) section.
### How can I validate the accuracy of my trained YOLO model?
To validate the accuracy of your trained YOLO11 model, you can use the `.val()` method in Python or the `yolo detect val` command in CLI. This will provide metrics like mAP50-95, mAP50, and more.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load the model
model = YOLO("path/to/best.pt")
# Validate the model
metrics = model.val()
print(metrics.box.map) # mAP50-95
```
=== "CLI"
```bash
yolo detect val model=path/to/best.pt
```
For more validation details, visit the [Val](../modes/val.md) page.
### What formats can I export a YOLO11 model to?
Ultralytics YOLO11 allows exporting models to various formats such as ONNX, TensorRT, CoreML, and more to ensure compatibility across different platforms and devices.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load the model
model = YOLO("yolo11n.pt")
# Export the model to ONNX format
model.export(format="onnx")
```
=== "CLI"
```bash
yolo export model=yolo11n.pt format=onnx
```
Check the full list of supported formats and instructions on the [Export](../modes/export.md) page.
### Why should I use Ultralytics YOLO11 for object detection?
Ultralytics YOLO11 is designed to offer state-of-the-art performance for object detection, segmentation, and pose estimation. Here are some key advantages:
1. **Pretrained Models**: Utilize models pretrained on popular datasets like COCO and ImageNet for faster development.
2. **High Accuracy**: Achieves impressive mAP scores, ensuring reliable object detection.
3. **Speed**: Optimized for real-time inference, making it ideal for applications requiring swift processing.
4. **Flexibility**: Export models to various formats like ONNX and TensorRT for deployment across multiple platforms.
Explore our [Blog](https://www.ultralytics.com/blog) for use cases and success stories showcasing YOLO11 in action.
docs/en/tasks/index.md 0 → 100644
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---
comments: true
description: Explore Ultralytics YOLO11 for detection, segmentation, classification, OBB, and pose estimation with high accuracy and speed. Learn how to apply each task.
keywords: Ultralytics YOLO11, detection, segmentation, classification, oriented object detection, pose estimation, computer vision, AI framework
---
# Ultralytics YOLO11 Tasks
<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-tasks-banner.avif" alt="Ultralytics YOLO supported tasks">
YOLO11 is an AI framework that supports multiple [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) **tasks**. The framework can be used to perform [detection](detect.md), [segmentation](segment.md), [obb](obb.md), [classification](classify.md), and [pose](pose.md) estimation. Each of these tasks has a different objective and use case.
<p align="center">
<br>
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/NAs-cfq9BDw"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Explore Ultralytics YOLO Tasks: <a href="https://www.ultralytics.com/glossary/object-detection">Object Detection</a>, Segmentation, OBB, Tracking, and Pose Estimation.
</p>
## [Detection](detect.md)
Detection is the primary task supported by YOLO11. It involves detecting objects in an image or video frame and drawing bounding boxes around them. The detected objects are classified into different categories based on their features. YOLO11 can detect multiple objects in a single image or video frame with high [accuracy](https://www.ultralytics.com/glossary/accuracy) and speed.
[Detection Examples](detect.md){ .md-button }
## [Segmentation](segment.md)
Segmentation is a task that involves segmenting an image into different regions based on the content of the image. Each region is assigned a label based on its content. This task is useful in applications such as [image segmentation](https://www.ultralytics.com/glossary/image-segmentation) and medical imaging. YOLO11 uses a variant of the U-Net architecture to perform segmentation.
[Segmentation Examples](segment.md){ .md-button }
## [Classification](classify.md)
Classification is a task that involves classifying an image into different categories. YOLO11 can be used to classify images based on their content. It uses a variant of the EfficientNet architecture to perform classification.
[Classification Examples](classify.md){ .md-button }
## [Pose](pose.md)
Pose/keypoint detection is a task that involves detecting specific points in an image or video frame. These points are referred to as keypoints and are used to track movement or pose estimation. YOLO11 can detect keypoints in an image or video frame with high accuracy and speed.
[Pose Examples](pose.md){ .md-button }
## [OBB](obb.md)
Oriented object detection goes a step further than regular object detection with introducing an extra angle to locate objects more accurate in an image. YOLO11 can detect rotated objects in an image or video frame with high accuracy and speed.
[Oriented Detection](obb.md){ .md-button }
## Conclusion
YOLO11 supports multiple tasks, including detection, segmentation, classification, oriented object detection and keypoints detection. Each of these tasks has different objectives and use cases. By understanding the differences between these tasks, you can choose the appropriate task for your computer vision application.
## FAQ
### What tasks can Ultralytics YOLO11 perform?
Ultralytics YOLO11 is a versatile AI framework capable of performing various computer vision tasks with high accuracy and speed. These tasks include:
- **[Detection](detect.md):** Identifying and localizing objects in images or video frames by drawing bounding boxes around them.
- **[Segmentation](segment.md):** Segmenting images into different regions based on their content, useful for applications like medical imaging.
- **[Classification](classify.md):** Categorizing entire images based on their content, leveraging variants of the EfficientNet architecture.
- **[Pose estimation](pose.md):** Detecting specific keypoints in an image or video frame to track movements or poses.
- **[Oriented Object Detection (OBB)](obb.md):** Detecting rotated objects with an added orientation angle for enhanced accuracy.
### How do I use Ultralytics YOLO11 for object detection?
To use Ultralytics YOLO11 for object detection, follow these steps:
1. Prepare your dataset in the appropriate format.
2. Train the YOLO11 model using the detection task.
3. Use the model to make predictions by feeding in new images or video frames.
!!! example
=== "Python"
```python
from ultralytics import YOLO
# Load a pre-trained YOLO model (adjust model type as needed)
model = YOLO("yolo11n.pt") # n, s, m, l, x versions available
# Perform object detection on an image
results = model.predict(source="image.jpg") # Can also use video, directory, URL, etc.
# Display the results
results[0].show() # Show the first image results
```
=== "CLI"
```bash
# Run YOLO detection from the command line
yolo detect model=yolo11n.pt source="image.jpg" # Adjust model and source as needed
```
For more detailed instructions, check out our [detection examples](detect.md).
### What are the benefits of using YOLO11 for segmentation tasks?
Using YOLO11 for segmentation tasks provides several advantages:
1. **High Accuracy:** The segmentation task leverages a variant of the U-Net architecture to achieve precise segmentation.
2. **Speed:** YOLO11 is optimized for real-time applications, offering quick processing even for high-resolution images.
3. **Multiple Applications:** It is ideal for medical imaging, autonomous driving, and other applications requiring detailed image segmentation.
Learn more about the benefits and use cases of YOLO11 for segmentation in the [segmentation section](segment.md).
### Can Ultralytics YOLO11 handle pose estimation and keypoint detection?
Yes, Ultralytics YOLO11 can effectively perform pose estimation and keypoint detection with high accuracy and speed. This feature is particularly useful for tracking movements in sports analytics, healthcare, and human-computer interaction applications. YOLO11 detects keypoints in an image or video frame, allowing for precise pose estimation.
For more details and implementation tips, visit our [pose estimation examples](pose.md).
### Why should I choose Ultralytics YOLO11 for oriented object detection (OBB)?
Oriented Object Detection (OBB) with YOLO11 provides enhanced [precision](https://www.ultralytics.com/glossary/precision) by detecting objects with an additional angle parameter. This feature is beneficial for applications requiring accurate localization of rotated objects, such as aerial imagery analysis and warehouse automation.
- **Increased Precision:** The angle component reduces false positives for rotated objects.
- **Versatile Applications:** Useful for tasks in geospatial analysis, robotics, etc.
Check out the [Oriented Object Detection section](obb.md) for more details and examples.
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