Update docs metadata (#3781)
This commit is contained in:
Glenn Jocher
GitHub
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---
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description: Explore Ultralytics cfg functions like cfg2dict, handle_deprecation, merge_equal_args & more to handle YOLO settings and configurations efficiently.
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keywords: Ultralytics, YOLO, Configuration, cfg2dict, handle_deprecation, merge_equals_args, handle_yolo_settings, copy_default_cfg, Image Detection
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---
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## cfg2dict
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---
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### ::: ultralytics.cfg.cfg2dict
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## copy_default_cfg
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---
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### ::: ultralytics.cfg.copy_default_cfg
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<br><br>
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<br><br>
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---
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description: Enhance your machine learning model with Ultralytics’ auto_annotate function. Simplify data annotation for improved model training.
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keywords: Ultralytics, Auto-Annotate, Machine Learning, AI, Annotation, Data Processing, Model Training
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---
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## auto_annotate
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---
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### ::: ultralytics.data.annotator.auto_annotate
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<br><br>
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<br><br>
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---
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description: Detailed exploration into Ultralytics data augmentation methods including BaseTransform, MixUp, LetterBox, ToTensor, and more for enhancing model performance.
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keywords: Ultralytics, Data Augmentation, BaseTransform, MixUp, RandomHSV, LetterBox, Albumentations, classify_transforms, classify_albumentations
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---
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## BaseTransform
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---
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### ::: ultralytics.data.augment.BaseTransform
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## classify_albumentations
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---
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### ::: ultralytics.data.augment.classify_albumentations
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<br><br>
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<br><br>
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---
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description: Explore BaseDataset in Ultralytics docs. Learn how this implementation simplifies dataset creation and manipulation.
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keywords: Ultralytics, docs, BaseDataset, data manipulation, dataset creation
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---
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## BaseDataset
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---
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### ::: ultralytics.data.base.BaseDataset
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<br><br>
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<br><br>
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---
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description: Explore the Ultralytics YOLO v3 data build procedures, including the InfiniteDataLoader, seed_worker, build_dataloader, and load_inference_source.
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keywords: Ultralytics, YOLO v3, Data build, DataLoader, InfiniteDataLoader, seed_worker, build_dataloader, load_inference_source
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---
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## InfiniteDataLoader
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---
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### ::: ultralytics.data.build.InfiniteDataLoader
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## load_inference_source
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---
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### ::: ultralytics.data.build.load_inference_source
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<br><br>
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<br><br>
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---
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description: Explore Ultralytics data converter functions like coco91_to_coco80_class, merge_multi_segment, rle2polygon for efficient data handling.
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keywords: Ultralytics, Data Converter, coco91_to_coco80_class, merge_multi_segment, rle2polygon
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---
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## coco91_to_coco80_class
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---
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### ::: ultralytics.data.converter.coco91_to_coco80_class
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## delete_dsstore
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---
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### ::: ultralytics.data.converter.delete_dsstore
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<br><br>
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<br><br>
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---
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description: Explore the YOLODataset and SemanticDataset classes in YOLO data. Learn how to efficiently handle and manipulate your data with Ultralytics.
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keywords: Ultralytics, YOLO, YOLODataset, SemanticDataset, data handling, data manipulation
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---
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## YOLODataset
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---
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### ::: ultralytics.data.dataset.YOLODataset
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## SemanticDataset
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---
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### ::: ultralytics.data.dataset.SemanticDataset
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<br><br>
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<br><br>
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---
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description: Find detailed guides on Ultralytics YOLO data loaders, including LoadStreams, LoadImages and LoadTensor. Learn how to get the best YouTube URLs.
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keywords: Ultralytics, data loaders, LoadStreams, LoadImages, LoadTensor, YOLO, YouTube URLs
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---
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## SourceTypes
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---
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### ::: ultralytics.data.loaders.SourceTypes
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## get_best_youtube_url
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---
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### ::: ultralytics.data.loaders.get_best_youtube_url
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<br><br>
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<br><br>
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---
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description: Uncover a detailed guide to Ultralytics data utilities. Learn functions from img2label_paths to autosplit, all boosting your YOLO model’s efficiency.
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keywords: Ultralytics, data utils, YOLO, img2label_paths, exif_size, polygon2mask, polygons2masks_overlap, check_cls_dataset, delete_dsstore, autosplit
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---
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## HUBDatasetStats
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---
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### ::: ultralytics.data.utils.HUBDatasetStats
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## autosplit
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---
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### ::: ultralytics.data.utils.autosplit
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<br><br>
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<br><br>
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---
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description: Explore the exporter functionality of Ultralytics. Learn about exporting formats, iOSDetectModel, and try exporting with examples.
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keywords: Ultralytics, Exporter, iOSDetectModel, Export Formats, Try export
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---
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## Exporter
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---
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### ::: ultralytics.engine.exporter.Exporter
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## export
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---
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### ::: ultralytics.engine.exporter.export
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<br><br>
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<br><br>
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---
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description: Explore the detailed guide on using the Ultralytics YOLO Engine Model. Learn better ways to implement, train and evaluate YOLO models.
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keywords: Ultralytics, YOLO, engine model, documentation, guide, implementation, training, evaluation
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---
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## YOLO
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---
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### ::: ultralytics.engine.model.YOLO
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<br><br>
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<br><br>
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---
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description: Learn about Ultralytics BasePredictor, an essential component of our engine that serves as the foundation for all prediction operations.
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keywords: Ultralytics, BasePredictor, YOLO, prediction, engine
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---
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## BasePredictor
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---
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### ::: ultralytics.engine.predictor.BasePredictor
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---
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description: Master Ultralytics engine results including base tensors, boxes, and keypoints with our thorough documentation.
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keywords: Ultralytics, engine, results, base tensor, boxes, keypoints
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---
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## BaseTensor
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---
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### ::: ultralytics.engine.results.BaseTensor
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## Probs
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---
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### ::: ultralytics.engine.results.Probs
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<br><br>
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<br><br>
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---
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description: Learn about the BaseTrainer class in the Ultralytics library. From training control, customization to advanced usage.
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keywords: Ultralytics, BaseTrainer, Machine Learning, Training Control, Python library
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---
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## BaseTrainer
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---
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### ::: ultralytics.engine.trainer.BaseTrainer
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---
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description: Learn about the Ultralytics BaseValidator module. Understand its principles, uses, and how it interacts with other components.
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keywords: Ultralytics, BaseValidator, Ultralytics engine, module, components
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---
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## BaseValidator
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---
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### ::: ultralytics.engine.validator.BaseValidator
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---
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description: Explore Ultralytics hub functions for model resetting, checking datasets, model exporting and more. Easy-to-follow instructions provided.
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keywords: Ultralytics, hub functions, model export, dataset check, reset model, YOLO Docs
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---
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## login
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---
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### ::: ultralytics.hub.login
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## check_dataset
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---
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### ::: ultralytics.hub.check_dataset
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---
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description: Dive into the Ultralytics Auth API documentation & learn how to manage authentication in your AI & ML projects easily and effectively.
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keywords: Ultralytics, Auth, API documentation, User Authentication, AI, Machine Learning
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---
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## Auth
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---
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### ::: ultralytics.hub.auth.Auth
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---
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description: Explore details about the HUBTrainingSession in Ultralytics framework. Learn to utilize this functionality for effective model training.
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keywords: Ultralytics, HUBTrainingSession, Documentation, Model Training, AI, Machine Learning, YOLO
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---
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## HUBTrainingSession
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---
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### ::: ultralytics.hub.session.HUBTrainingSession
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---
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description: Explore Ultralytics docs for various Events, including "request_with_credentials" and "requests_with_progress". Also, understand the use of the "smart_request".
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keywords: Ultralytics, Events, request_with_credentials, smart_request, Ultralytics hub utils, requests_with_progress
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---
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## Events
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---
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### ::: ultralytics.hub.utils.Events
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## smart_request
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---
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### ::: ultralytics.hub.utils.smart_request
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---
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description: Learn all about Ultralytics FastSAM model. Dive into our comprehensive guide for seamless integration and efficient model training.
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keywords: Ultralytics, FastSAM model, Model documentation, Efficient model training
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---
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## FastSAM
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---
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### ::: ultralytics.models.fastsam.model.FastSAM
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---
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description: Get detailed insights about Ultralytics FastSAMPredictor. Learn to predict and optimize your AI models with our properly documented guidelines.
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keywords: Ultralytics, FastSAMPredictor, predictive modeling, AI optimization, machine learning, deep learning, Ultralytics documentation
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---
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## FastSAMPredictor
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---
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### ::: ultralytics.models.fastsam.predict.FastSAMPredictor
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---
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description: Learn to effectively utilize FastSAMPrompt model from Ultralytics. Detailed guide to help you get the most out of your machine learning models.
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keywords: Ultralytics, FastSAMPrompt, machine learning, model, guide, documentation
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---
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## FastSAMPrompt
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---
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### ::: ultralytics.models.fastsam.prompt.FastSAMPrompt
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---
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description: Learn how to adjust bounding boxes to image borders in Ultralytics models using the bbox_iou utility. Enhance your object detection performance.
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keywords: Ultralytics, bounding boxes, Bboxes, image borders, object detection, bbox_iou, model utilities
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---
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## adjust_bboxes_to_image_border
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---
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### ::: ultralytics.models.fastsam.utils.adjust_bboxes_to_image_border
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## bbox_iou
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---
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### ::: ultralytics.models.fastsam.utils.bbox_iou
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---
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description: Learn about FastSAMValidator in Ultralytics models. Comprehensive guide to enhancing AI capabilities with Ultralytics.
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keywords: Ultralytics, FastSAMValidator, model, synthetic, AI, machine learning, validation
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---
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## FastSAMValidator
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---
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### ::: ultralytics.models.fastsam.val.FastSAMValidator
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---
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description: Learn how our NAS model operates in Ultralytics. Comprehensive guide with detailed examples. Master the nuances of Ultralytics NAS model.
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keywords: Ultralytics, NAS model, NAS guide, machine learning, model documentation
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---
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## NAS
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---
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### ::: ultralytics.models.nas.model.NAS
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---
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description: Explore Ultralytics NASPredictor. Understand high-level architecture of the model for effective implementation and efficient predictions.
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keywords: NASPredictor, Ultralytics, Ultralytics model, model architecture, efficient predictions
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---
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## NASPredictor
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---
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### ::: ultralytics.models.nas.predict.NASPredictor
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---
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description: Explore the utilities and functions of the Ultralytics NASValidator. Find out how it benefits allocation and optimization in AI models.
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keywords: Ultralytics, NASValidator, models.nas.val.NASValidator, AI models, allocation, optimization
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---
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## NASValidator
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---
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### ::: ultralytics.models.nas.val.NASValidator
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---
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description: Explore the specifics of using the RTDETR model in Ultralytics. Detailed documentation layered with explanations and examples.
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keywords: Ultralytics, RTDETR model, Ultralytics models, object detection, Ultralytics documentation
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---
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## RTDETR
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---
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### ::: ultralytics.models.rtdetr.model.RTDETR
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---
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description: Learn how to use the RTDETRPredictor model of the Ultralytics package. Detailed documentation, usage instructions, and advice.
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keywords: Ultralytics, RTDETRPredictor, model documentation, guide, real-time object detection
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---
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## RTDETRPredictor
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---
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### ::: ultralytics.models.rtdetr.predict.RTDETRPredictor
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<br><br>
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<br><br>
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---
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description: Get insights into RTDETRTrainer, a crucial component of Ultralytics for effective model training. Explore detailed documentation at Ultralytics.
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keywords: Ultralytics, RTDETRTrainer, model training, Ultralytics models, PyTorch models, neural networks, machine learning, deep learning
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---
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## RTDETRTrainer
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---
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### ::: ultralytics.models.rtdetr.train.RTDETRTrainer
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## train
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---
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### ::: ultralytics.models.rtdetr.train.train
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<br><br>
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<br><br>
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---
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description: Explore RTDETRDataset in Ultralytics Models. Learn about the RTDETRValidator function, understand its usage in real-time object detection.
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keywords: Ultralytics, RTDETRDataset, RTDETRValidator, real-time object detection, models documentation
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---
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## RTDETRDataset
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---
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### ::: ultralytics.models.rtdetr.val.RTDETRDataset
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## RTDETRValidator
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---
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### ::: ultralytics.models.rtdetr.val.RTDETRValidator
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<br><br>
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<br><br>
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---
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description: Explore Ultralytics methods for mask data processing, transformation and encoding. Deepen your understanding of RLE encoding, image cropping and more.
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keywords: Ultralytics, Mask Data, Transformation, Encoding, RLE encoding, Image cropping, Pytorch, SAM, AMG, Ultralytics model
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---
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## MaskData
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---
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### ::: ultralytics.models.sam.amg.MaskData
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## batched_mask_to_box
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---
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### ::: ultralytics.models.sam.amg.batched_mask_to_box
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<br><br>
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<br><br>
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---
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description: Master building SAM ViT models with Ultralytics. Discover steps to leverage the power of SAM and Vision Transformer sessions.
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keywords: Ultralytics, SAM, build sam, vision transformer, vits, build_sam_vit_l, build_sam_vit_b, build_sam
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---
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## build_sam_vit_h
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---
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### ::: ultralytics.models.sam.build.build_sam_vit_h
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## build_sam
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---
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### ::: ultralytics.models.sam.build.build_sam
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<br><br>
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<br><br>
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@ -1,4 +1,9 @@
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||||
---
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||||
description: Dive into the SAM model details in the Ultralytics YOLO documentation. Understand, implement, and optimize your model use.
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keywords: Ultralytics, YOLO, SAM Model, Documentations, Machine Learning, AI, Convolutional neural network
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---
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## SAM
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||||
---
|
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### ::: ultralytics.models.sam.model.SAM
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<br><br>
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<br><br>
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@ -1,3 +1,8 @@
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---
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description: Explore MaskDecoder, a part of the Ultralytics models. Gain insights on how to utilize it effectively in the SAM modules decoders MLP.
|
||||
keywords: Ultralytics, MaskDecoder, SAM modules, decoders, MLP, YOLO, machine learning, image recognition
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---
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## MaskDecoder
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---
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### ::: ultralytics.models.sam.modules.decoders.MaskDecoder
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## MLP
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||||
---
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||||
### ::: ultralytics.models.sam.modules.decoders.MLP
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<br><br>
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<br><br>
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---
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description: Discover detailed information on ImageEncoderViT, PositionEmbeddingRandom, Attention, window_partition, get_rel_pos and more in Ultralytics models encoders documentation.
|
||||
keywords: Ultralytics, Encoders, Modules, Documentation, ImageEncoderViT, PositionEmbeddingRandom, Attention, window_partition, get_rel_pos
|
||||
---
|
||||
|
||||
## ImageEncoderViT
|
||||
---
|
||||
### ::: ultralytics.models.sam.modules.encoders.ImageEncoderViT
|
||||
@ -46,4 +51,4 @@
|
||||
## add_decomposed_rel_pos
|
||||
---
|
||||
### ::: ultralytics.models.sam.modules.encoders.add_decomposed_rel_pos
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,4 +1,9 @@
|
||||
---
|
||||
description: Explore the Sam module of Ultralytics. Discover detailed methods, classes, and information for efficient deep-learning model training!.
|
||||
keywords: Ultralytics, Sam module, deep learning, model training, Ultralytics documentation
|
||||
---
|
||||
|
||||
## Sam
|
||||
---
|
||||
### ::: ultralytics.models.sam.modules.sam.Sam
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Get in-depth insights about Ultralytics Tiny Encoder Modules such as Conv2d_BN, MBConv, ConvLayer, Attention, BasicLayer, and TinyViT. Improve your understanding of machine learning model components.
|
||||
keywords: Ultralytics, Tiny Encoder, Conv2d_BN, MBConv, ConvLayer, Attention, BasicLayer, TinyViT, Machine learning modules, Ultralytics models
|
||||
---
|
||||
|
||||
## Conv2d_BN
|
||||
---
|
||||
### ::: ultralytics.models.sam.modules.tiny_encoder.Conv2d_BN
|
||||
@ -51,4 +56,4 @@
|
||||
## TinyViT
|
||||
---
|
||||
### ::: ultralytics.models.sam.modules.tiny_encoder.TinyViT
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Learn about TwoWayTransformer and Attention modules in Ultralytics. Leverage these tools to enhance your AI models.
|
||||
keywords: Ultralytics, TwoWayTransformer, Attention, AI models, transformers
|
||||
---
|
||||
|
||||
## TwoWayTransformer
|
||||
---
|
||||
### ::: ultralytics.models.sam.modules.transformer.TwoWayTransformer
|
||||
@ -11,4 +16,4 @@
|
||||
## Attention
|
||||
---
|
||||
### ::: ultralytics.models.sam.modules.transformer.Attention
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,4 +1,9 @@
|
||||
---
|
||||
description: Master the ultralytics.models.sam.predict.Predictor class with our comprehensive guide. Discover techniques to enhance your model predictions.
|
||||
keywords: Ultralytics, predictor, models, sam.predict.Predictor, AI, machine learning, predictive models
|
||||
---
|
||||
|
||||
## Predictor
|
||||
---
|
||||
### ::: ultralytics.models.sam.predict.Predictor
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Learn to use the DETRLoss function provided by Ultralytics YOLO. Understand how to utilize loss in RTDETR detection models to improve accuracy.
|
||||
keywords: Ultralytics, YOLO, Documentation, DETRLoss, Detection Loss, Loss function, DETR, RTDETR Detection Models
|
||||
---
|
||||
|
||||
## DETRLoss
|
||||
---
|
||||
### ::: ultralytics.models.utils.loss.DETRLoss
|
||||
@ -6,4 +11,4 @@
|
||||
## RTDETRDetectionLoss
|
||||
---
|
||||
### ::: ultralytics.models.utils.loss.RTDETRDetectionLoss
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Discover details for "HungarianMatcher" & "inverse_sigmoid" functions in Ultralytics YOLO, advanced tools supporting detection models.
|
||||
keywords: Ultralytics, YOLO, HungarianMatcher, inverse_sigmoid, detection models, model utilities, ops
|
||||
---
|
||||
|
||||
## HungarianMatcher
|
||||
---
|
||||
### ::: ultralytics.models.utils.ops.HungarianMatcher
|
||||
@ -11,4 +16,4 @@
|
||||
## inverse_sigmoid
|
||||
---
|
||||
### ::: ultralytics.models.utils.ops.inverse_sigmoid
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore the Ultralytics ClassificationPredictor guide for model prediction and visualization. Build powerful AI models with YOLO.
|
||||
keywords: Ultralytics, classification predictor, predict, YOLO, AI models, model visualization
|
||||
---
|
||||
|
||||
## ClassificationPredictor
|
||||
---
|
||||
### ::: ultralytics.models.yolo.classify.predict.ClassificationPredictor
|
||||
@ -6,4 +11,4 @@
|
||||
## predict
|
||||
---
|
||||
### ::: ultralytics.models.yolo.classify.predict.predict
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Delve into Classification Trainer at Ultralytics YOLO docs and optimize your model's training process with insights from the masters!.
|
||||
keywords: Ultralytics, YOLO, Classification Trainer, deep learning, training process, AI models, documentation
|
||||
---
|
||||
|
||||
## ClassificationTrainer
|
||||
---
|
||||
### ::: ultralytics.models.yolo.classify.train.ClassificationTrainer
|
||||
@ -6,4 +11,4 @@
|
||||
## train
|
||||
---
|
||||
### ::: ultralytics.models.yolo.classify.train.train
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore YOLO ClassificationValidator, a key element of Ultralytics YOLO models. Learn how it validates and fine-tunes model outputs.
|
||||
keywords: Ultralytics, YOLO, ClassificationValidator, model validation, model fine-tuning, deep learning, computer vision
|
||||
---
|
||||
|
||||
## ClassificationValidator
|
||||
---
|
||||
### ::: ultralytics.models.yolo.classify.val.ClassificationValidator
|
||||
@ -6,4 +11,4 @@
|
||||
## val
|
||||
---
|
||||
### ::: ultralytics.models.yolo.classify.val.val
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore the guide to using the DetectionPredictor in Ultralytics YOLO. Learn how to predict, detect and analyze objects accurately.
|
||||
keywords: Ultralytics, YOLO, DetectionPredictor, detect, predict, object detection, analysis
|
||||
---
|
||||
|
||||
## DetectionPredictor
|
||||
---
|
||||
### ::: ultralytics.models.yolo.detect.predict.DetectionPredictor
|
||||
@ -6,4 +11,4 @@
|
||||
## predict
|
||||
---
|
||||
### ::: ultralytics.models.yolo.detect.predict.predict
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Maximize your model's potential with Ultralytics YOLO Detection Trainer. Learn advanced techniques, tips, and tricks for training.
|
||||
keywords: Ultralytics YOLO, YOLO, Detection Trainer, Model Training, Machine Learning, Deep Learning, Computer Vision
|
||||
---
|
||||
|
||||
## DetectionTrainer
|
||||
---
|
||||
### ::: ultralytics.models.yolo.detect.train.DetectionTrainer
|
||||
@ -6,4 +11,4 @@
|
||||
## train
|
||||
---
|
||||
### ::: ultralytics.models.yolo.detect.train.train
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Discover function valuation of your YOLO models with the Ultralytics Detection Validator. Enhance precision and recall rates today.
|
||||
keywords: Ultralytics, YOLO, Detection Validator, model valuation, precision, recall
|
||||
---
|
||||
|
||||
## DetectionValidator
|
||||
---
|
||||
### ::: ultralytics.models.yolo.detect.val.DetectionValidator
|
||||
@ -6,4 +11,4 @@
|
||||
## val
|
||||
---
|
||||
### ::: ultralytics.models.yolo.detect.val.val
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Discover how to use PosePredictor in the Ultralytics YOLO model. Includes detailed guides, code examples, and explanations.
|
||||
keywords: Ultralytics, YOLO, PosePredictor, machine learning, AI, predictive models
|
||||
---
|
||||
|
||||
## PosePredictor
|
||||
---
|
||||
### ::: ultralytics.models.yolo.pose.predict.PosePredictor
|
||||
@ -6,4 +11,4 @@
|
||||
## predict
|
||||
---
|
||||
### ::: ultralytics.models.yolo.pose.predict.predict
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore Ultralytics PoseTrainer for YOLO models. Get a step-by-step guide on how to train on custom pose data for more accurate AI modeling.
|
||||
keywords: Ultralytics, YOLO, PoseTrainer, pose training, AI modeling, custom data training
|
||||
---
|
||||
|
||||
## PoseTrainer
|
||||
---
|
||||
### ::: ultralytics.models.yolo.pose.train.PoseTrainer
|
||||
@ -6,4 +11,4 @@
|
||||
## train
|
||||
---
|
||||
### ::: ultralytics.models.yolo.pose.train.train
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore the PoseValidator—review how Ultralytics YOLO validates poses for object detection. Improve your understanding of YOLO.
|
||||
keywords: PoseValidator, Ultralytics, YOLO, Object detection, Pose validation
|
||||
---
|
||||
|
||||
## PoseValidator
|
||||
---
|
||||
### ::: ultralytics.models.yolo.pose.val.PoseValidator
|
||||
@ -6,4 +11,4 @@
|
||||
## val
|
||||
---
|
||||
### ::: ultralytics.models.yolo.pose.val.val
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Discover how to utilize the YOLO Segmentation Predictor in Ultralytics. Enhance your objects detection skills with us.
|
||||
keywords: YOLO, Ultralytics, object detection, segmentation predictor
|
||||
---
|
||||
|
||||
## SegmentationPredictor
|
||||
---
|
||||
### ::: ultralytics.models.yolo.segment.predict.SegmentationPredictor
|
||||
@ -6,4 +11,4 @@
|
||||
## predict
|
||||
---
|
||||
### ::: ultralytics.models.yolo.segment.predict.predict
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Maximize your YOLO model's performance with our SegmentationTrainer. Explore comprehensive guides and tutorials on ultralytics.com.
|
||||
keywords: Ultralytics, YOLO, SegmentationTrainer, image segmentation, object detection, model training, YOLO model
|
||||
---
|
||||
|
||||
## SegmentationTrainer
|
||||
---
|
||||
### ::: ultralytics.models.yolo.segment.train.SegmentationTrainer
|
||||
@ -6,4 +11,4 @@
|
||||
## train
|
||||
---
|
||||
### ::: ultralytics.models.yolo.segment.train.train
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Get practical insights about our SegmentationValidator in YOLO Ultralytics models. Discover functionality details, methods, inputs, and outputs.
|
||||
keywords: Ultralytics, YOLO, SegmentationValidator, model segmentation, image classification, object detection
|
||||
---
|
||||
|
||||
## SegmentationValidator
|
||||
---
|
||||
### ::: ultralytics.models.yolo.segment.val.SegmentationValidator
|
||||
@ -6,4 +11,4 @@
|
||||
## val
|
||||
---
|
||||
### ::: ultralytics.models.yolo.segment.val.val
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Get to know more about Ultralytics nn.autobackend.check_class_names functionality. Optimize your YOLO models seamlessly.
|
||||
keywords: Ultralytics, AutoBackend, check_class_names, YOLO, YOLO models, optimization
|
||||
---
|
||||
|
||||
## AutoBackend
|
||||
---
|
||||
### ::: ultralytics.nn.autobackend.AutoBackend
|
||||
@ -6,4 +11,4 @@
|
||||
## check_class_names
|
||||
---
|
||||
### ::: ultralytics.nn.autobackend.check_class_names
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore Ultralytics YOLO neural network modules, Proto to BottleneckCSP. Detailed explanation of each module with easy-to-follow code examples.
|
||||
keywords: YOLO, Ultralytics, neural network, nn.modules.block, Proto, HGBlock, SPPF, C2, C3, RepC3, C3Ghost, Bottleneck, BottleneckCSP
|
||||
---
|
||||
|
||||
## DFL
|
||||
---
|
||||
### ::: ultralytics.nn.modules.block.DFL
|
||||
@ -81,4 +86,4 @@
|
||||
## BottleneckCSP
|
||||
---
|
||||
### ::: ultralytics.nn.modules.block.BottleneckCSP
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore various Ultralytics convolution modules including Conv2, DWConv, ConvTranspose, GhostConv, Channel Attention and more.
|
||||
keywords: Ultralytics, Convolution Modules, Conv2, DWConv, ConvTranspose, GhostConv, ChannelAttention, CBAM, autopad
|
||||
---
|
||||
|
||||
## Conv
|
||||
---
|
||||
### ::: ultralytics.nn.modules.conv.Conv
|
||||
@ -66,4 +71,4 @@
|
||||
## autopad
|
||||
---
|
||||
### ::: ultralytics.nn.modules.conv.autopad
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore docs covering Ultralytics YOLO detection, pose & RTDETRDecoder. Comprehensive guides to help you understand Ultralytics nn modules.
|
||||
keywords: Ultralytics, YOLO, Detection, Pose, RTDETRDecoder, nn modules, guides
|
||||
---
|
||||
|
||||
## Detect
|
||||
---
|
||||
### ::: ultralytics.nn.modules.head.Detect
|
||||
@ -21,4 +26,4 @@
|
||||
## RTDETRDecoder
|
||||
---
|
||||
### ::: ultralytics.nn.modules.head.RTDETRDecoder
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Learn about Ultralytics transformer encoder, layer, MLP block, LayerNorm2d and the deformable transformer decoder layer. Expand your understanding of these crucial AI modules.
|
||||
keywords: Ultralytics, Ultralytics documentation, TransformerEncoderLayer, TransformerLayer, MLPBlock, LayerNorm2d, DeformableTransformerDecoderLayer
|
||||
---
|
||||
|
||||
## TransformerEncoderLayer
|
||||
---
|
||||
### ::: ultralytics.nn.modules.transformer.TransformerEncoderLayer
|
||||
@ -46,4 +51,4 @@
|
||||
## DeformableTransformerDecoder
|
||||
---
|
||||
### ::: ultralytics.nn.modules.transformer.DeformableTransformerDecoder
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore Ultralytics neural network utils, such as bias_init_with_prob, inverse_sigmoid and multi_scale_deformable_attn_pytorch functions.
|
||||
keywords: Ultralytics, neural network, nn.modules.utils, bias_init_with_prob, inverse_sigmoid, multi_scale_deformable_attn_pytorch
|
||||
---
|
||||
|
||||
## _get_clones
|
||||
---
|
||||
### ::: ultralytics.nn.modules.utils._get_clones
|
||||
@ -21,4 +26,4 @@
|
||||
## multi_scale_deformable_attn_pytorch
|
||||
---
|
||||
### ::: ultralytics.nn.modules.utils.multi_scale_deformable_attn_pytorch
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore Ultralytics YOLO docs to understand task-specific models like DetectionModel, PoseModel, RTDETRDetectionModel and more. Plus, learn about ensemble, model loading.
|
||||
keywords: Ultralytics, YOLO docs, DetectionModel, SegmentationModel, ClassificationModel, Ensemble, torch_safe_load, yaml_model_load, guess_model_task
|
||||
---
|
||||
|
||||
## BaseModel
|
||||
---
|
||||
### ::: ultralytics.nn.tasks.BaseModel
|
||||
@ -71,4 +76,4 @@
|
||||
## guess_model_task
|
||||
---
|
||||
### ::: ultralytics.nn.tasks.guess_model_task
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Get familiar with TrackState in Ultralytics. Learn how it is used in the BaseTrack of the Ultralytics tracker for enhanced functionality.
|
||||
keywords: Ultralytics, TrackState, BaseTrack, Ultralytics tracker, Ultralytics documentation
|
||||
---
|
||||
|
||||
## TrackState
|
||||
---
|
||||
### ::: ultralytics.trackers.basetrack.TrackState
|
||||
@ -6,4 +11,4 @@
|
||||
## BaseTrack
|
||||
---
|
||||
### ::: ultralytics.trackers.basetrack.BaseTrack
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Master the use of Ultralytics BOTrack, a key component of the powerful Ultralytics tracking system. Learn to integrate and use BOTSORT in your projects.
|
||||
keywords: Ultralytics, BOTSORT, BOTrack, tracking system, official documentation, machine learning, AI tracking
|
||||
---
|
||||
|
||||
## BOTrack
|
||||
---
|
||||
### ::: ultralytics.trackers.bot_sort.BOTrack
|
||||
@ -6,4 +11,4 @@
|
||||
## BOTSORT
|
||||
---
|
||||
### ::: ultralytics.trackers.bot_sort.BOTSORT
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Step-in to explore in-depth the functionalities of Ultralytics BYTETracker under STrack. Gain advanced feature insights to streamline your operations.
|
||||
keywords: STrack, Ultralytics, BYTETracker, documentation, Ultralytics tracker, object tracking, YOLO
|
||||
---
|
||||
|
||||
## STrack
|
||||
---
|
||||
### ::: ultralytics.trackers.byte_tracker.STrack
|
||||
@ -6,4 +11,4 @@
|
||||
## BYTETracker
|
||||
---
|
||||
### ::: ultralytics.trackers.byte_tracker.BYTETracker
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore Ultralytics documentation on prediction function starters & register trackers. Understand our code & its applications better.
|
||||
keywords: Ultralytics, YOLO, on predict start, register tracker, prediction functions, documentation
|
||||
---
|
||||
|
||||
## on_predict_start
|
||||
---
|
||||
### ::: ultralytics.trackers.track.on_predict_start
|
||||
@ -11,4 +16,4 @@
|
||||
## register_tracker
|
||||
---
|
||||
### ::: ultralytics.trackers.track.register_tracker
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,4 +1,9 @@
|
||||
---
|
||||
description: Explore the Ultralytics GMC tool in our comprehensive documentation. Learn how it works, best practices, and implementation advice.
|
||||
keywords: Ultralytics, GMC utility, Ultralytics documentation, Ultralytics tracker, machine learning tools
|
||||
---
|
||||
|
||||
## GMC
|
||||
---
|
||||
### ::: ultralytics.trackers.utils.gmc.GMC
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore KalmanFilterXYAH, a key component of Ultralytics trackers. Understand its utilities and learn to leverage it in your own projects.
|
||||
keywords: Ultralytics, KalmanFilterXYAH, tracker, documentation, guide
|
||||
---
|
||||
|
||||
## KalmanFilterXYAH
|
||||
---
|
||||
### ::: ultralytics.trackers.utils.kalman_filter.KalmanFilterXYAH
|
||||
@ -6,4 +11,4 @@
|
||||
## KalmanFilterXYWH
|
||||
---
|
||||
### ::: ultralytics.trackers.utils.kalman_filter.KalmanFilterXYWH
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore in-depth guidance for using Ultralytics trackers utils matching, including merge_matches, linear_assignment, iou_distance, embedding_distance, fuse_motion, and fuse_score.
|
||||
keywords: Ultralytics, Trackers Utils, Matching, merge_matches, linear_assignment, iou_distance, embedding_distance, fuse_motion, fuse_score, documentation
|
||||
---
|
||||
|
||||
## merge_matches
|
||||
---
|
||||
### ::: ultralytics.trackers.utils.matching.merge_matches
|
||||
@ -56,4 +61,4 @@
|
||||
## bbox_ious
|
||||
---
|
||||
### ::: ultralytics.trackers.utils.matching.bbox_ious
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore the Ultralytics Utils package, with handy functions like colorstr, yaml_save, set_logging & more, designed to enhance your coding experience.
|
||||
keywords: Ultralytics, Utils, utilitarian functions, colorstr, yaml_save, set_logging, is_kaggle, is_docker, clean_url
|
||||
---
|
||||
|
||||
## SimpleClass
|
||||
---
|
||||
### ::: ultralytics.utils.SimpleClass
|
||||
@ -166,4 +171,4 @@
|
||||
## url2file
|
||||
---
|
||||
### ::: ultralytics.utils.url2file
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore Ultralytics documentation for check_train_batch_size utility in the autobatch module. Understand how it could improve your machine learning process.
|
||||
keywords: Ultralytics, check_train_batch_size, autobatch, utility, machine learning, documentation
|
||||
---
|
||||
|
||||
## check_train_batch_size
|
||||
---
|
||||
### ::: ultralytics.utils.autobatch.check_train_batch_size
|
||||
@ -6,4 +11,4 @@
|
||||
## autobatch
|
||||
---
|
||||
### ::: ultralytics.utils.autobatch.autobatch
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Discover how to profile your models using Ultralytics utilities. Enhance performance, optimize your benchmarks, and learn best practices.
|
||||
keywords: Ultralytics, ProfileModels, benchmarks, model profiling, performance optimization
|
||||
---
|
||||
|
||||
## ProfileModels
|
||||
---
|
||||
### ::: ultralytics.utils.benchmarks.ProfileModels
|
||||
@ -6,4 +11,4 @@
|
||||
## benchmark
|
||||
---
|
||||
### ::: ultralytics.utils.benchmarks.benchmark
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore how to use the on-train, on-validation, on-pretrain, and on-predict callbacks in Ultralytics. Learn to update params, save models, and add integration callbacks.
|
||||
keywords: Ultralytics, Callbacks, On-train, On-validation, On-pretrain, On-predict, Parameters update, Model saving, Integration callbacks
|
||||
---
|
||||
|
||||
## on_pretrain_routine_start
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.base.on_pretrain_routine_start
|
||||
@ -131,4 +136,4 @@
|
||||
## add_integration_callbacks
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.base.add_integration_callbacks
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Uncover the specifics of Ultralytics ClearML callbacks, from pretrain routine start to training end. Boost your ML model performance.
|
||||
keywords: Ultralytics, clearML, callbacks, pretrain routine start, validation end, train epoch end, training end
|
||||
---
|
||||
|
||||
## _log_debug_samples
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.clearml._log_debug_samples
|
||||
@ -31,4 +36,4 @@
|
||||
## on_train_end
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.clearml.on_train_end
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore comprehensive documentation for utilising Comet Callbacks in Ultralytics. Learn to optimise training, logging, and experiment workflows.
|
||||
keywords: Ultralytics, Comet Callbacks, Training optimisation, Logging, Experiment Workflows
|
||||
---
|
||||
|
||||
## _get_comet_mode
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.comet._get_comet_mode
|
||||
@ -116,4 +121,4 @@
|
||||
## on_train_end
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.comet.on_train_end
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Browse through Ultralytics YOLO docs to learn about important logging and callback functions used in training and pretraining models.
|
||||
keywords: Ultralytics, YOLO, callbacks, logger, training, pretraining, machine learning, models
|
||||
---
|
||||
|
||||
## _logger_disabled
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.dvc._logger_disabled
|
||||
@ -46,4 +51,4 @@
|
||||
## on_train_end
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.dvc.on_train_end
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore the detailed information on key Ultralytics callbacks such as on_pretrain_routine_end, on_model_save, on_train_start, and on_predict_start.
|
||||
keywords: Ultralytics, callbacks, on_pretrain_routine_end, on_model_save, on_train_start, on_predict_start, hub, training
|
||||
---
|
||||
|
||||
## on_pretrain_routine_end
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.hub.on_pretrain_routine_end
|
||||
@ -36,4 +41,4 @@
|
||||
## on_export_start
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.hub.on_export_start
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Understand routines at the end of pre-training and training in Ultralytics. Elevate your MLflow callbacks expertise.
|
||||
keywords: Ultralytics, MLflow, Callbacks, on_pretrain_routine_end, on_train_end, Machine Learning, Training
|
||||
---
|
||||
|
||||
## on_pretrain_routine_end
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.mlflow.on_pretrain_routine_end
|
||||
@ -11,4 +16,4 @@
|
||||
## on_train_end
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.mlflow.on_train_end
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore exhaustive details about Ultralytics callbacks in Neptune, with specifics about scalar logging, routine start, and more.
|
||||
keywords: Ultralytics, Neptune callbacks, on_train_epoch_end, on_val_end, _log_plot, _log_images, on_pretrain_routine_start, on_fit_epoch_end, on_train_end
|
||||
---
|
||||
|
||||
## _log_scalars
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.neptune._log_scalars
|
||||
@ -36,4 +41,4 @@
|
||||
## on_train_end
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.neptune.on_train_end
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,4 +1,9 @@
|
||||
---
|
||||
description: Discover the functionality of the on_fit_epoch_end callback in the Ultralytics YOLO framework. Learn how to end an epoch in your deep learning projects.
|
||||
keywords: Ultralytics, YOLO, on_fit_epoch_end, callbacks, documentation, deep learning, YOLO framework
|
||||
---
|
||||
|
||||
## on_fit_epoch_end
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.raytune.on_fit_epoch_end
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore Ultralytics YOLO Docs for a deep understanding of log_scalars, on_batch_end & other callback utilities embedded in the tensorboard module.
|
||||
keywords: Ultralytics, YOLO, documentation, callback utilities, log_scalars, on_batch_end, tensorboard
|
||||
---
|
||||
|
||||
## _log_scalars
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.tensorboard._log_scalars
|
||||
@ -16,4 +21,4 @@
|
||||
## on_fit_epoch_end
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.tensorboard.on_fit_epoch_end
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Deep dive into Ultralytics callbacks. Learn how to use the _log_plots, on_fit_epoch_end, and on_train_end functions effectively.
|
||||
keywords: Ultralytics, callbacks, _log_plots, on_fit_epoch_end, on_train_end
|
||||
---
|
||||
|
||||
## _log_plots
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.wb._log_plots
|
||||
@ -21,4 +26,4 @@
|
||||
## on_train_end
|
||||
---
|
||||
### ::: ultralytics.utils.callbacks.wb.on_train_end
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Learn about our routine checks that safeguard Ultralytics operations including ASCII, font, YOLO file, YAML, Python and torchvision checks.
|
||||
keywords: Ultralytics, utility checks, ASCII, check_version, pip_update, check_python, check_torchvision, check_yaml, YOLO filename
|
||||
---
|
||||
|
||||
## is_ascii
|
||||
---
|
||||
### ::: ultralytics.utils.checks.is_ascii
|
||||
@ -86,4 +91,4 @@
|
||||
## print_args
|
||||
---
|
||||
### ::: ultralytics.utils.checks.print_args
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Discover the role of dist.find_free_network_port & dist.generate_ddp_command in Ultralytics DDP utilities. Use our guide for efficient deployment.
|
||||
keywords: Ultralytics, DDP, DDP utility functions, Distributed Data Processing, find free network port, generate DDP command
|
||||
---
|
||||
|
||||
## find_free_network_port
|
||||
---
|
||||
### ::: ultralytics.utils.dist.find_free_network_port
|
||||
@ -16,4 +21,4 @@
|
||||
## ddp_cleanup
|
||||
---
|
||||
### ::: ultralytics.utils.dist.ddp_cleanup
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Learn about the download utilities in Ultralytics YOLO, featuring functions like is_url, check_disk_space, get_github_assets, and download.
|
||||
keywords: Ultralytics, YOLO, download utilities, is_url, check_disk_space, get_github_assets, download, documentation
|
||||
---
|
||||
|
||||
## is_url
|
||||
---
|
||||
### ::: ultralytics.utils.downloads.is_url
|
||||
@ -31,4 +36,4 @@
|
||||
## download
|
||||
---
|
||||
### ::: ultralytics.utils.downloads.download
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,4 +1,9 @@
|
||||
---
|
||||
description: Learn about the HUBModelError in Ultralytics. Enhance your understanding, troubleshoot errors and optimize your machine learning projects.
|
||||
keywords: Ultralytics, HUBModelError, Machine Learning, Error troubleshooting, Ultralytics documentation
|
||||
---
|
||||
|
||||
## HUBModelError
|
||||
---
|
||||
### ::: ultralytics.utils.errors.HUBModelError
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Discover how to use Ultralytics utility functions for file-related operations including incrementing paths, finding file age, checking file size and creating directories.
|
||||
keywords: Ultralytics, utility functions, file operations, working directory, file age, file size, create directories
|
||||
---
|
||||
|
||||
## WorkingDirectory
|
||||
---
|
||||
### ::: ultralytics.utils.files.WorkingDirectory
|
||||
@ -31,4 +36,4 @@
|
||||
## make_dirs
|
||||
---
|
||||
### ::: ultralytics.utils.files.make_dirs
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Dive into Ultralytics detailed utility guide. Learn about Bboxes, _ntuple and more from Ultralytics utils.instance module.
|
||||
keywords: Ultralytics, Bboxes, _ntuple, utility, ultralytics utils.instance
|
||||
---
|
||||
|
||||
## Bboxes
|
||||
---
|
||||
### ::: ultralytics.utils.instance.Bboxes
|
||||
@ -11,4 +16,4 @@
|
||||
## _ntuple
|
||||
---
|
||||
### ::: ultralytics.utils.instance._ntuple
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore Ultralytics' versatile loss functions - VarifocalLoss, BboxLoss, v8DetectionLoss, v8PoseLoss. Improve your accuracy on YOLO implementations.
|
||||
keywords: Ultralytics, Loss functions, VarifocalLoss, BboxLoss, v8DetectionLoss, v8PoseLoss, YOLO, Ultralytics Documentation
|
||||
---
|
||||
|
||||
## VarifocalLoss
|
||||
---
|
||||
### ::: ultralytics.utils.loss.VarifocalLoss
|
||||
@ -36,4 +41,4 @@
|
||||
## v8ClassificationLoss
|
||||
---
|
||||
### ::: ultralytics.utils.loss.v8ClassificationLoss
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore Ultralytics YOLO metrics tools - from confusion matrix, detection metrics, pose metrics to box IOU. Learn how to compute and plot precision-recall curves.
|
||||
keywords: Ultralytics, YOLO, YOLOv3, YOLOv4, metrics, confusion matrix, detection metrics, pose metrics, box IOU, mask IOU, plot precision-recall curves, compute average precision
|
||||
---
|
||||
|
||||
## ConfusionMatrix
|
||||
---
|
||||
### ::: ultralytics.utils.metrics.ConfusionMatrix
|
||||
@ -86,4 +91,4 @@
|
||||
## ap_per_class
|
||||
---
|
||||
### ::: ultralytics.utils.metrics.ap_per_class
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore detailed documentation for Ultralytics utility operations. Learn about methods like segment2box, make_divisible, clip_boxes, and many more.
|
||||
keywords: Ultralytics YOLO, Utility Operations, segment2box, make_divisible, clip_boxes, scale_image, xywh2xyxy, xyxy2xywhn, xywh2ltwh, ltwh2xywh, segments2boxes, crop_mask, process_mask, scale_masks, masks2segments
|
||||
---
|
||||
|
||||
## Profile
|
||||
---
|
||||
### ::: ultralytics.utils.ops.Profile
|
||||
@ -136,4 +141,4 @@
|
||||
## clean_str
|
||||
---
|
||||
### ::: ultralytics.utils.ops.clean_str
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Learn about Ultralytics utils patches including imread, imshow and torch_save. Enhance your image processing skills.
|
||||
keywords: Ultralytics, Utils, Patches, imread, imshow, torch_save, image processing
|
||||
---
|
||||
|
||||
## imread
|
||||
---
|
||||
### ::: ultralytics.utils.patches.imread
|
||||
@ -16,4 +21,4 @@
|
||||
## torch_save
|
||||
---
|
||||
### ::: ultralytics.utils.patches.torch_save
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Master advanced plotting utils from Ultralytics including color annotations, label and image plotting, and feature visualization.
|
||||
keywords: Ultralytics, plotting, utils, color annotation, label plotting, image plotting, feature visualization
|
||||
---
|
||||
|
||||
## Colors
|
||||
---
|
||||
### ::: ultralytics.utils.plotting.Colors
|
||||
@ -36,4 +41,4 @@
|
||||
## feature_visualization
|
||||
---
|
||||
### ::: ultralytics.utils.plotting.feature_visualization
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore Ultralytics utilities for optimized task assignment, bounding box creation, and distance calculation. Learn more about algorithm implementations.
|
||||
keywords: Ultralytics, task aligned assigner, select highest overlaps, make anchors, dist2bbox, bbox2dist, utilities, algorithm
|
||||
---
|
||||
|
||||
## TaskAlignedAssigner
|
||||
---
|
||||
### ::: ultralytics.utils.tal.TaskAlignedAssigner
|
||||
@ -26,4 +31,4 @@
|
||||
## bbox2dist
|
||||
---
|
||||
### ::: ultralytics.utils.tal.bbox2dist
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,8 @@
|
||||
---
|
||||
description: Explore Ultralytics-tailored torch utility features like Model EMA, early stopping, smart inference, image scaling, get_flops, and many more.
|
||||
keywords: Ultralytics, Torch Utils, Model EMA, Early Stopping, Smart Inference, Get CPU Info, Time Sync, Fuse Deconv and bn, Get num params, Get FLOPs, Scale img, Copy attr, Intersect dicts, De_parallel, Init seeds, Profile
|
||||
---
|
||||
|
||||
## ModelEMA
|
||||
---
|
||||
### ::: ultralytics.utils.torch_utils.ModelEMA
|
||||
@ -131,4 +136,4 @@
|
||||
## profile
|
||||
---
|
||||
### ::: ultralytics.utils.torch_utils.profile
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,4 +1,9 @@
|
||||
---
|
||||
description: Learn to utilize the run_ray_tune function with Ultralytics. Make your machine learning tuning process easier and more efficient.
|
||||
keywords: Ultralytics, run_ray_tune, machine learning tuning, machine learning efficiency
|
||||
---
|
||||
|
||||
## run_ray_tune
|
||||
---
|
||||
### ::: ultralytics.utils.tuner.run_ray_tune
|
||||
<br><br>
|
||||
<br><br>
|
||||
Reference in New Issue
Block a user