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Docs updates for HUB, YOLOv4, YOLOv7, NAS (#3174)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Sergiu Waxmann
2023-06-15 21:17:10 +02:00
committed by GitHub
parent c340f84ce9
commit 2f02d8ea53
179 changed files with 786 additions and 206 deletions
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3
docs/reference/hub/auth.md
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---
description: Learn how to use Ultralytics hub authentication in your projects with examples and guidelines from the Auth page on Ultralytics Docs.
keywords: Ultralytics, ultralytics hub, api keys, authentication, collab accounts, requests, hub management, monitoring
---
# Auth
---
:::ultralytics.hub.auth.Auth
<br><br>
<br><br>

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docs/reference/hub/session.md
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description: Accelerate your AI development with the Ultralytics HUB Training Session. High-performance training of object detection models.
keywords: YOLOv5, object detection, HUBTrainingSession, custom models, Ultralytics Docs
---
# HUBTrainingSession
---
:::ultralytics.hub.session.HUBTrainingSession
<br><br>
<br><br>

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docs/reference/hub/utils.md
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---
description: Explore Ultralytics events, including 'request_with_credentials' and 'smart_request', to improve your project's performance and efficiency.
keywords: Ultralytics, Hub Utils, API Documentation, Python, requests_with_progress, Events, classes, usage, examples
---
# Events
@ -20,4 +21,4 @@ description: Explore Ultralytics events, including 'request_with_credentials' an
# smart_request
---
:::ultralytics.hub.utils.smart_request
<br><br>
<br><br>

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docs/reference/nn/autobackend.md
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---
description: Ensure class names match filenames for easy imports. Use AutoBackend to automatically rename and refactor model files.
keywords: AutoBackend, ultralytics, nn, autobackend, check class names, neural network
---
# AutoBackend
@ -10,4 +11,4 @@ description: Ensure class names match filenames for easy imports. Use AutoBacken
# check_class_names
---
:::ultralytics.nn.autobackend.check_class_names
<br><br>
<br><br>

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docs/reference/nn/autoshape.md
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---
description: Detect 80+ object categories with bounding box coordinates and class probabilities using AutoShape in Ultralytics YOLO. Explore Detections now.
keywords: Ultralytics, YOLO, docs, autoshape, detections, object detection, customized shapes, bounding boxes, computer vision
---
# AutoShape
@ -10,4 +11,4 @@ description: Detect 80+ object categories with bounding box coordinates and clas
# Detections
---
:::ultralytics.nn.autoshape.Detections
<br><br>
<br><br>

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docs/reference/nn/modules/block.md
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---
description: Explore ultralytics.nn.modules.block to build powerful YOLO object detection models. Master DFL, HGStem, SPP, CSP components and more.
keywords: Ultralytics, NN Modules, Blocks, DFL, HGStem, SPP, C1, C2f, C3x, C3TR, GhostBottleneck, BottleneckCSP, Computer Vision
---
# DFL
@ -85,4 +86,4 @@ description: Explore ultralytics.nn.modules.block to build powerful YOLO object
# BottleneckCSP
---
:::ultralytics.nn.modules.block.BottleneckCSP
<br><br>
<br><br>

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docs/reference/nn/modules/conv.md
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---
description: Explore convolutional neural network modules & techniques such as LightConv, DWConv, ConvTranspose, GhostConv, CBAM & autopad with Ultralytics Docs.
keywords: Ultralytics, Convolutional Neural Network, Conv2, DWConv, ConvTranspose, GhostConv, ChannelAttention, CBAM, autopad
---
# Conv
@ -70,4 +71,4 @@ description: Explore convolutional neural network modules & techniques such as L
# autopad
---
:::ultralytics.nn.modules.conv.autopad
<br><br>
<br><br>

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docs/reference/nn/modules/head.md
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---
description: 'Learn about Ultralytics YOLO modules: Segment, Classify, and RTDETRDecoder. Optimize object detection and classification in your project.'
keywords: Ultralytics, YOLO, object detection, pose estimation, RTDETRDecoder, modules, classes, documentation
---
# Detect
@ -25,4 +26,4 @@ description: 'Learn about Ultralytics YOLO modules: Segment, Classify, and RTDET
# RTDETRDecoder
---
:::ultralytics.nn.modules.head.RTDETRDecoder
<br><br>
<br><br>

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docs/reference/nn/modules/transformer.md
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description: Explore the Ultralytics nn modules pages on Transformer and MLP blocks, LayerNorm2d, and Deformable Transformer Decoder Layer.
keywords: Ultralytics, NN Modules, TransformerEncoderLayer, TransformerLayer, MLPBlock, LayerNorm2d, DeformableTransformerDecoderLayer, examples, code snippets, tutorials
---
# TransformerEncoderLayer
@ -50,4 +51,4 @@ description: Explore the Ultralytics nn modules pages on Transformer and MLP blo
# DeformableTransformerDecoder
---
:::ultralytics.nn.modules.transformer.DeformableTransformerDecoder
<br><br>
<br><br>

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docs/reference/nn/modules/utils.md
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---
description: 'Learn about Ultralytics NN modules: get_clones, linear_init_, and multi_scale_deformable_attn_pytorch. Code examples and usage tips.'
keywords: Ultralytics, NN Utils, Docs, PyTorch, bias initialization, linear initialization, multi-scale deformable attention
---
# _get_clones
@ -25,4 +26,4 @@ description: 'Learn about Ultralytics NN modules: get_clones, linear_init_, and
# multi_scale_deformable_attn_pytorch
---
:::ultralytics.nn.modules.utils.multi_scale_deformable_attn_pytorch
<br><br>
<br><br>

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docs/reference/nn/tasks.md
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---
description: Learn how to work with Ultralytics YOLO Detection, Segmentation & Classification Models, load weights and parse models in PyTorch.
keywords: neural network, deep learning, computer vision, object detection, image segmentation, image classification, model ensemble, PyTorch
---
# BaseModel
@ -70,4 +71,4 @@ description: Learn how to work with Ultralytics YOLO Detection, Segmentation & C
# guess_model_task
---
:::ultralytics.nn.tasks.guess_model_task
<br><br>
<br><br>

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docs/reference/tracker/track.md
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---
description: Learn how to register custom event-tracking and track predictions with Ultralytics YOLO via on_predict_start and register_tracker methods.
keywords: Ultralytics YOLO, tracker registration, on_predict_start, object detection
---
# on_predict_start
@ -15,4 +16,4 @@ description: Learn how to register custom event-tracking and track predictions w
# register_tracker
---
:::ultralytics.tracker.track.register_tracker
<br><br>
<br><br>

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docs/reference/tracker/trackers/basetrack.md
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---
description: 'TrackState: A comprehensive guide to Ultralytics tracker''s BaseTrack for monitoring model performance. Improve your tracking capabilities now!'
keywords: object detection, object tracking, Ultralytics YOLO, TrackState, workflow improvement
---
# TrackState
@ -10,4 +11,4 @@ description: 'TrackState: A comprehensive guide to Ultralytics tracker''s BaseTr
# BaseTrack
---
:::ultralytics.tracker.trackers.basetrack.BaseTrack
<br><br>
<br><br>

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docs/reference/tracker/trackers/bot_sort.md
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---
description: '"Optimize tracking with Ultralytics BOTrack. Easily sort and track bots with BOTSORT. Streamline data collection for improved performance."'
keywords: BOTrack, Ultralytics YOLO Docs, features, usage
---
# BOTrack
@ -10,4 +11,4 @@ description: '"Optimize tracking with Ultralytics BOTrack. Easily sort and track
# BOTSORT
---
:::ultralytics.tracker.trackers.bot_sort.BOTSORT
<br><br>
<br><br>

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docs/reference/tracker/trackers/byte_tracker.md
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description: Learn how to track ByteAI model sizes and tips for model optimization with STrack, a byte tracking tool from Ultralytics.
keywords: Byte Tracker, Ultralytics STrack, application monitoring, bytes sent, bytes received, code examples, setup instructions
---
# STrack
@ -10,4 +11,4 @@ description: Learn how to track ByteAI model sizes and tips for model optimizati
# BYTETracker
---
:::ultralytics.tracker.trackers.byte_tracker.BYTETracker
<br><br>
<br><br>

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docs/reference/tracker/utils/gmc.md
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---
description: '"Track Google Marketing Campaigns in GMC with Ultralytics Tracker. Learn to set up and use GMC for detailed analytics. Get started now."'
keywords: Ultralytics, YOLO, object detection, tracker, optimization, models, documentation
---
# GMC
---
:::ultralytics.tracker.utils.gmc.GMC
<br><br>
<br><br>

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docs/reference/tracker/utils/kalman_filter.md
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---
description: Improve object tracking with KalmanFilterXYAH in Ultralytics YOLO - an efficient and accurate algorithm for state estimation.
keywords: KalmanFilterXYAH, Ultralytics Docs, Kalman filter algorithm, object tracking, computer vision, YOLO
---
# KalmanFilterXYAH
@ -10,4 +11,4 @@ description: Improve object tracking with KalmanFilterXYAH in Ultralytics YOLO -
# KalmanFilterXYWH
---
:::ultralytics.tracker.utils.kalman_filter.KalmanFilterXYWH
<br><br>
<br><br>

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docs/reference/tracker/utils/matching.md
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description: Learn how to match and fuse object detections for accurate target tracking using Ultralytics' YOLO merge_matches, iou_distance, and embedding_distance.
keywords: Ultralytics, multi-object tracking, object tracking, detection, recognition, matching, indices, iou distance, gate cost matrix, fuse iou, bbox ious
---
# merge_matches
@ -60,4 +61,4 @@ description: Learn how to match and fuse object detections for accurate target t
# bbox_ious
---
:::ultralytics.tracker.utils.matching.bbox_ious
<br><br>
<br><br>

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docs/reference/yolo/data/annotator.md
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description: Learn how to use auto_annotate in Ultralytics YOLO to generate annotations automatically for your dataset. Simplify object detection workflows.
keywords: Ultralytics YOLO, Auto Annotator, AI, image annotation, object detection, labelling, tool
---
# auto_annotate
---
:::ultralytics.yolo.data.annotator.auto_annotate
<br><br>
<br><br>

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docs/reference/yolo/data/augment.md
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---
description: Use Ultralytics YOLO Data Augmentation transforms with Base, MixUp, and Albumentations for object detection and classification.
keywords: YOLO, data augmentation, transforms, BaseTransform, MixUp, RandomHSV, Albumentations, ToTensor, classify_transforms, classify_albumentations
---
# BaseTransform
@ -95,4 +96,4 @@ description: Use Ultralytics YOLO Data Augmentation transforms with Base, MixUp,
# classify_albumentations
---
:::ultralytics.yolo.data.augment.classify_albumentations
<br><br>
<br><br>

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docs/reference/yolo/data/base.md
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description: Learn about BaseDataset in Ultralytics YOLO, a flexible dataset class for object detection. Maximize your YOLO performance with custom datasets.
keywords: BaseDataset, Ultralytics YOLO, object detection, real-world applications, documentation
---
# BaseDataset
---
:::ultralytics.yolo.data.base.BaseDataset
<br><br>
<br><br>

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docs/reference/yolo/data/build.md
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---
description: Maximize YOLO performance with Ultralytics' InfiniteDataLoader, seed_worker, build_dataloader, and load_inference_source functions.
keywords: Ultralytics, YOLO, object detection, data loading, build dataloader, load inference source
---
# InfiniteDataLoader
@ -35,4 +36,4 @@ description: Maximize YOLO performance with Ultralytics' InfiniteDataLoader, see
# load_inference_source
---
:::ultralytics.yolo.data.build.load_inference_source
<br><br>
<br><br>

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docs/reference/yolo/data/converter.md
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---
description: Convert COCO-91 to COCO-80 class, RLE to polygon, and merge multi-segment images with Ultralytics YOLO data converter. Improve your object detection.
keywords: Ultralytics, YOLO, converter, COCO91, COCO80, rle2polygon, merge_multi_segment, annotations
---
# coco91_to_coco80_class
@ -30,4 +31,4 @@ description: Convert COCO-91 to COCO-80 class, RLE to polygon, and merge multi-s
# delete_dsstore
---
:::ultralytics.yolo.data.converter.delete_dsstore
<br><br>
<br><br>

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docs/reference/yolo/data/dataloaders/stream_loaders.md
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description: 'Ultralytics YOLO Docs: Learn about stream loaders for image and tensor data, as well as autocasting techniques. Check out SourceTypes and more.'
keywords: Ultralytics YOLO, data loaders, stream load images, screenshots, tensor data, autocast list, youtube URL retriever
---
# SourceTypes
@ -40,4 +41,4 @@ description: 'Ultralytics YOLO Docs: Learn about stream loaders for image and te
# get_best_youtube_url
---
:::ultralytics.yolo.data.dataloaders.stream_loaders.get_best_youtube_url
<br><br>
<br><br>

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docs/reference/yolo/data/dataloaders/v5augmentations.md
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---
description: Enhance image data with Albumentations CenterCrop, normalize, augment_hsv, replicate, random_perspective, cutout, & box_candidates.
keywords: YOLO, object detection, data loaders, V5 augmentations, CenterCrop, normalize, random_perspective
---
# Albumentations
@ -85,4 +86,4 @@ description: Enhance image data with Albumentations CenterCrop, normalize, augme
# classify_transforms
---
:::ultralytics.yolo.data.dataloaders.v5augmentations.classify_transforms
<br><br>
<br><br>

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docs/reference/yolo/data/dataloaders/v5loader.md
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---
description: Efficiently load images and labels to models using Ultralytics YOLO's InfiniteDataLoader, LoadScreenshots, and LoadStreams.
keywords: YOLO, data loader, image classification, object detection, Ultralytics
---
# InfiniteDataLoader
@ -90,4 +91,4 @@ description: Efficiently load images and labels to models using Ultralytics YOLO
# create_classification_dataloader
---
:::ultralytics.yolo.data.dataloaders.v5loader.create_classification_dataloader
<br><br>
<br><br>

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docs/reference/yolo/data/dataset.md
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---
description: Create custom YOLOv5 datasets with Ultralytics YOLODataset and SemanticDataset. Streamline your object detection and segmentation projects.
keywords: YOLODataset, SemanticDataset, Ultralytics YOLO Docs, Object Detection, Segmentation
---
# YOLODataset
@ -15,4 +16,4 @@ description: Create custom YOLOv5 datasets with Ultralytics YOLODataset and Sema
# SemanticDataset
---
:::ultralytics.yolo.data.dataset.SemanticDataset
<br><br>
<br><br>

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docs/reference/yolo/data/dataset_wrappers.md
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---
description: Create a custom dataset of mixed and oriented rectangular objects with Ultralytics YOLO's MixAndRectDataset.
keywords: Ultralytics YOLO, MixAndRectDataset, dataset wrapper, image-level annotations, object-level annotations, rectangular object detection
---
# MixAndRectDataset
---
:::ultralytics.yolo.data.dataset_wrappers.MixAndRectDataset
<br><br>
<br><br>

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docs/reference/yolo/data/utils.md
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---
description: Efficiently handle data in YOLO with Ultralytics. Utilize HUBDatasetStats and customize dataset with these data utility functions.
keywords: YOLOv4, Object Detection, Computer Vision, Deep Learning, Convolutional Neural Network, CNN, Ultralytics Docs
---
# HUBDatasetStats
@ -65,4 +66,4 @@ description: Efficiently handle data in YOLO with Ultralytics. Utilize HUBDatase
# zip_directory
---
:::ultralytics.yolo.data.utils.zip_directory
<br><br>
<br><br>

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docs/reference/yolo/engine/exporter.md
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description: Learn how to export your YOLO model in various formats using Ultralytics' exporter package - iOS, GDC, and more.
keywords: Ultralytics, YOLO, exporter, iOS detect model, gd_outputs, export
---
# Exporter
@ -30,4 +31,4 @@ description: Learn how to export your YOLO model in various formats using Ultral
# export
---
:::ultralytics.yolo.engine.exporter.export
<br><br>
<br><br>

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docs/reference/yolo/engine/model.md
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---
description: Discover the YOLO model of Ultralytics engine to simplify your object detection tasks with state-of-the-art models.
keywords: YOLO, object detection, model, architecture, usage, customization, Ultralytics Docs
---
# YOLO
---
:::ultralytics.yolo.engine.model.YOLO
<br><br>
<br><br>

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docs/reference/yolo/engine/predictor.md
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description: '"The BasePredictor class in Ultralytics YOLO Engine predicts object detection in images and videos. Learn to implement YOLO with ease."'
keywords: Ultralytics, YOLO, BasePredictor, Object Detection, Computer Vision, Fast Model, Insights
---
# BasePredictor
---
:::ultralytics.yolo.engine.predictor.BasePredictor
<br><br>
<br><br>

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docs/reference/yolo/engine/results.md
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---
description: Learn about BaseTensor & Boxes in Ultralytics YOLO Engine. Check out Ultralytics Docs for quality tutorials and resources on object detection.
keywords: YOLO, Engine, Results, Masks, Probs, Ultralytics
---
# BaseTensor
@ -21,3 +22,13 @@ description: Learn about BaseTensor & Boxes in Ultralytics YOLO Engine. Check ou
---
:::ultralytics.yolo.engine.results.Masks
<br><br>
# Keypoints
---
:::ultralytics.yolo.engine.results.Keypoints
<br><br>
# Probs
---
:::ultralytics.yolo.engine.results.Probs
<br><br>

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docs/reference/yolo/engine/trainer.md
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---
description: Train faster with mixed precision. Learn how to use BaseTrainer with Advanced Mixed Precision to optimize YOLOv3 and YOLOv4 models.
keywords: Ultralytics YOLO, BaseTrainer, object detection models, training guide
---
# BaseTrainer
---
:::ultralytics.yolo.engine.trainer.BaseTrainer
<br><br>
# check_amp
---
:::ultralytics.yolo.engine.trainer.check_amp
<br><br>
<br><br>

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docs/reference/yolo/engine/validator.md
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---
description: Ensure YOLOv5 models meet constraints and standards with the BaseValidator class. Learn how to use it here.
keywords: Ultralytics, YOLO, BaseValidator, models, validation, object detection
---
# BaseValidator
---
:::ultralytics.yolo.engine.validator.BaseValidator
<br><br>
<br><br>

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docs/reference/yolo/nas/model.md Normal file
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description: Learn about the Neural Architecture Search (NAS) feature available in Ultralytics YOLO. Find out how NAS can improve object detection models and increase accuracy. Get started today!.
keywords: Ultralytics YOLO, object detection, NAS, Neural Architecture Search, model optimization, accuracy improvement
---
# NAS
---
:::ultralytics.yolo.nas.model.NAS
<br><br>

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docs/reference/yolo/nas/predict.md Normal file
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description: Learn how to use NASPredictor in Ultralytics YOLO for deploying efficient CNN models with search algorithms in neural architecture search.
keywords: Ultralytics YOLO, NASPredictor, neural architecture search, efficient CNN models, search algorithms
---
# NASPredictor
---
:::ultralytics.yolo.nas.predict.NASPredictor
<br><br>

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docs/reference/yolo/nas/val.md Normal file
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description: Learn about NASValidator in the Ultralytics YOLO Docs. Properly validate YOLO neural architecture search results for optimal performance.
keywords: NASValidator, YOLO, neural architecture search, validation, performance, Ultralytics
---
# NASValidator
---
:::ultralytics.yolo.nas.val.NASValidator
<br><br>

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docs/reference/yolo/utils/autobatch.md
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---
description: Dynamically adjusts input size to optimize GPU memory usage during training. Learn how to use check_train_batch_size with Ultralytics YOLO.
keywords: YOLOv5, batch size, training, Ultralytics Autobatch, object detection, model performance
---
# check_train_batch_size
@ -10,4 +11,4 @@ description: Dynamically adjusts input size to optimize GPU memory usage during
# autobatch
---
:::ultralytics.yolo.utils.autobatch.autobatch
<br><br>
<br><br>

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docs/reference/yolo/utils/benchmarks.md
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@ -1,5 +1,6 @@
---
description: Improve your YOLO's performance and measure its speed. Benchmark utility for YOLOv5.
keywords: Ultralytics YOLO, ProfileModels, benchmark, model inference, detection
---
# ProfileModels
@ -10,4 +11,4 @@ description: Improve your YOLO's performance and measure its speed. Benchmark ut
# benchmark
---
:::ultralytics.yolo.utils.benchmarks.benchmark
<br><br>
<br><br>

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docs/reference/yolo/utils/callbacks/base.md
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@ -1,5 +1,6 @@
---
description: Learn about YOLO's callback functions from on_train_start to add_integration_callbacks. See how these callbacks modify and save models.
keywords: YOLO, Ultralytics, callbacks, object detection, training, inference
---
# on_pretrain_routine_start
@ -135,4 +136,4 @@ description: Learn about YOLO's callback functions from on_train_start to add_in
# add_integration_callbacks
---
:::ultralytics.yolo.utils.callbacks.base.add_integration_callbacks
<br><br>
<br><br>

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docs/reference/yolo/utils/callbacks/clearml.md
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@ -1,5 +1,6 @@
---
description: Improve your YOLOv5 model training with callbacks from ClearML. Learn about log debug samples, pre-training routines, validation and more.
keywords: Ultralytics YOLO, callbacks, log plots, epoch monitoring, training end events
---
# _log_debug_samples
@ -35,4 +36,4 @@ description: Improve your YOLOv5 model training with callbacks from ClearML. Lea
# on_train_end
---
:::ultralytics.yolo.utils.callbacks.clearml.on_train_end
<br><br>
<br><br>

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docs/reference/yolo/utils/callbacks/comet.md
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@ -1,5 +1,6 @@
---
description: Learn about YOLO callbacks using the Comet.ml platform, enhancing object detection training and testing with custom logging and visualizations.
keywords: Ultralytics, YOLO, callbacks, Comet ML, log images, log predictions, log plots, fetch metadata, fetch annotations, create experiment data, format experiment data
---
# _get_comet_mode
@ -120,4 +121,4 @@ description: Learn about YOLO callbacks using the Comet.ml platform, enhancing o
# on_train_end
---
:::ultralytics.yolo.utils.callbacks.comet.on_train_end
<br><br>
<br><br>

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docs/reference/yolo/utils/callbacks/dvc.md Normal file
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---
description: Explore Ultralytics YOLO Utils DVC Callbacks such as logging images, plots, confusion matrices, and training progress.
keywords: Ultralytics, YOLO, Utils, DVC, Callbacks, images, plots, confusion matrices, training progress
---
# _logger_disabled
---
:::ultralytics.yolo.utils.callbacks.dvc._logger_disabled
<br><br>
# _log_images
---
:::ultralytics.yolo.utils.callbacks.dvc._log_images
<br><br>
# _log_plots
---
:::ultralytics.yolo.utils.callbacks.dvc._log_plots
<br><br>
# _log_confusion_matrix
---
:::ultralytics.yolo.utils.callbacks.dvc._log_confusion_matrix
<br><br>
# on_pretrain_routine_start
---
:::ultralytics.yolo.utils.callbacks.dvc.on_pretrain_routine_start
<br><br>
# on_pretrain_routine_end
---
:::ultralytics.yolo.utils.callbacks.dvc.on_pretrain_routine_end
<br><br>
# on_train_start
---
:::ultralytics.yolo.utils.callbacks.dvc.on_train_start
<br><br>
# on_train_epoch_start
---
:::ultralytics.yolo.utils.callbacks.dvc.on_train_epoch_start
<br><br>
# on_fit_epoch_end
---
:::ultralytics.yolo.utils.callbacks.dvc.on_fit_epoch_end
<br><br>
# on_train_end
---
:::ultralytics.yolo.utils.callbacks.dvc.on_train_end
<br><br>

3
docs/reference/yolo/utils/callbacks/hub.md
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@ -1,5 +1,6 @@
---
description: Improve YOLOv5 model training with Ultralytics' on-train callbacks. Boost performance on-pretrain-routine-end, model-save, train/predict start.
keywords: Ultralytics, YOLO, callbacks, on_pretrain_routine_end, on_fit_epoch_end, on_train_start, on_val_start, on_predict_start, on_export_start
---
# on_pretrain_routine_end
@ -40,4 +41,4 @@ description: Improve YOLOv5 model training with Ultralytics' on-train callbacks.
# on_export_start
---
:::ultralytics.yolo.utils.callbacks.hub.on_export_start
<br><br>
<br><br>

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docs/reference/yolo/utils/callbacks/mlflow.md
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---
description: Track model performance and metrics with MLflow in YOLOv5. Use callbacks like on_pretrain_routine_end or on_train_end to log information.
keywords: Ultralytics, YOLO, Utils, MLflow, callbacks, on_pretrain_routine_end, on_train_end, Tracking, Model Management, training
---
# on_pretrain_routine_end
@ -15,4 +16,4 @@ description: Track model performance and metrics with MLflow in YOLOv5. Use call
# on_train_end
---
:::ultralytics.yolo.utils.callbacks.mlflow.on_train_end
<br><br>
<br><br>

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docs/reference/yolo/utils/callbacks/neptune.md
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@ -1,5 +1,6 @@
---
description: Improve YOLOv5 training with Neptune, a powerful logging tool. Track metrics like images, plots, and epochs for better model performance.
keywords: Ultralytics, YOLO, Neptune, Callbacks, log scalars, log images, log plots, training, validation
---
# _log_scalars
@ -40,4 +41,4 @@ description: Improve YOLOv5 training with Neptune, a powerful logging tool. Trac
# on_train_end
---
:::ultralytics.yolo.utils.callbacks.neptune.on_train_end
<br><br>
<br><br>

3
docs/reference/yolo/utils/callbacks/raytune.md
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---
description: '"Improve YOLO model performance with on_fit_epoch_end callback. Learn to integrate with Ray Tune for hyperparameter tuning. Ultralytics YOLO docs."'
keywords: on_fit_epoch_end, Ultralytics YOLO, callback function, training, model tuning
---
# on_fit_epoch_end
---
:::ultralytics.yolo.utils.callbacks.raytune.on_fit_epoch_end
<br><br>
<br><br>

3
docs/reference/yolo/utils/callbacks/tensorboard.md
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@ -1,5 +1,6 @@
---
description: Learn how to monitor the training process with Tensorboard using Ultralytics YOLO's "_log_scalars" and "on_batch_end" methods.
keywords: TensorBoard callbacks, YOLO training, ultralytics YOLO
---
# _log_scalars
@ -20,4 +21,4 @@ description: Learn how to monitor the training process with Tensorboard using Ul
# on_fit_epoch_end
---
:::ultralytics.yolo.utils.callbacks.tensorboard.on_fit_epoch_end
<br><br>
<br><br>

8
docs/reference/yolo/utils/callbacks/wb.md
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@ -1,7 +1,13 @@
---
description: Learn how to use Ultralytics YOLO's built-in callbacks `on_pretrain_routine_start` and `on_train_epoch_end` for improved training performance.
keywords: Ultralytics, YOLO, callbacks, weights, biases, training
---
# _log_plots
---
:::ultralytics.yolo.utils.callbacks.wb._log_plots
<br><br>
# on_pretrain_routine_start
---
:::ultralytics.yolo.utils.callbacks.wb.on_pretrain_routine_start
@ -20,4 +26,4 @@ description: Learn how to use Ultralytics YOLO's built-in callbacks `on_pretrain
# on_train_end
---
:::ultralytics.yolo.utils.callbacks.wb.on_train_end
<br><br>
<br><br>

8
docs/reference/yolo/utils/checks.md
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@ -1,5 +1,6 @@
---
description: 'Check functions for YOLO utils: image size, version, font, requirements, filename suffix, YAML file, YOLO, and Git version.'
keywords: YOLO, Ultralytics, Utils, Checks, image sizing, version updates, font compatibility, Python requirements, file suffixes, YAML syntax, image showing, AMP
---
# is_ascii
@ -72,6 +73,11 @@ description: 'Check functions for YOLO utils: image size, version, font, require
:::ultralytics.yolo.utils.checks.check_yolo
<br><br>
# check_amp
---
:::ultralytics.yolo.utils.checks.check_amp
<br><br>
# git_describe
---
:::ultralytics.yolo.utils.checks.git_describe
@ -80,4 +86,4 @@ description: 'Check functions for YOLO utils: image size, version, font, require
# print_args
---
:::ultralytics.yolo.utils.checks.print_args
<br><br>
<br><br>

3
docs/reference/yolo/utils/dist.md
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@ -1,5 +1,6 @@
---
description: Learn how to find free network port and generate DDP (Distributed Data Parallel) command in Ultralytics YOLO with easy examples.
keywords: ultralytics, YOLO, utils, dist, distributed deep learning, DDP file, DDP cleanup
---
# find_free_network_port
@ -20,4 +21,4 @@ description: Learn how to find free network port and generate DDP (Distributed D
# ddp_cleanup
---
:::ultralytics.yolo.utils.dist.ddp_cleanup
<br><br>
<br><br>

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docs/reference/yolo/utils/downloads.md
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@ -1,5 +1,6 @@
---
description: Download and unzip YOLO pretrained models. Ultralytics YOLO docs utils.downloads.unzip_file, checks disk space, downloads and attempts assets.
keywords: Ultralytics YOLO, downloads, trained models, datasets, weights, deep learning, computer vision
---
# is_url
@ -30,4 +31,4 @@ description: Download and unzip YOLO pretrained models. Ultralytics YOLO docs ut
# download
---
:::ultralytics.yolo.utils.downloads.download
<br><br>
<br><br>

3
docs/reference/yolo/utils/errors.md
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@ -1,8 +1,9 @@
---
description: Learn about HUBModelError in Ultralytics YOLO Docs. Resolve the error and get the most out of your YOLO model.
keywords: HUBModelError, Ultralytics YOLO, YOLO Documentation, Object detection errors, YOLO Errors, HUBModelError Solutions
---
# HUBModelError
---
:::ultralytics.yolo.utils.errors.HUBModelError
<br><br>
<br><br>

3
docs/reference/yolo/utils/files.md
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@ -1,5 +1,6 @@
---
description: 'Learn about Ultralytics YOLO files and directory utilities: WorkingDirectory, file_age, file_size, and make_dirs.'
keywords: YOLO, object detection, file utils, file age, file size, working directory, make directories, Ultralytics Docs
---
# WorkingDirectory
@ -35,4 +36,4 @@ description: 'Learn about Ultralytics YOLO files and directory utilities: Workin
# make_dirs
---
:::ultralytics.yolo.utils.files.make_dirs
<br><br>
<br><br>

3
docs/reference/yolo/utils/instance.md
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@ -1,5 +1,6 @@
---
description: Learn about Bounding Boxes (Bboxes) and _ntuple in Ultralytics YOLO for object detection. Improve accuracy and speed with these powerful tools.
keywords: Ultralytics, YOLO, Bboxes, _ntuple, object detection, instance segmentation
---
# Bboxes
@ -15,4 +16,4 @@ description: Learn about Bounding Boxes (Bboxes) and _ntuple in Ultralytics YOLO
# _ntuple
---
:::ultralytics.yolo.utils.instance._ntuple
<br><br>
<br><br>

3
docs/reference/yolo/utils/loss.md
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@ -1,5 +1,6 @@
---
description: Learn about Varifocal Loss and Keypoint Loss in Ultralytics YOLO for advanced bounding box and pose estimation. Visit our docs for more.
keywords: Ultralytics, YOLO, loss functions, object detection, keypoint detection, segmentation, classification
---
# VarifocalLoss
@ -35,4 +36,4 @@ description: Learn about Varifocal Loss and Keypoint Loss in Ultralytics YOLO fo
# v8ClassificationLoss
---
:::ultralytics.yolo.utils.loss.v8ClassificationLoss
<br><br>
<br><br>

3
docs/reference/yolo/utils/metrics.md
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@ -1,5 +1,6 @@
---
description: Explore Ultralytics YOLO's FocalLoss, DetMetrics, PoseMetrics, ClassifyMetrics, and more with Ultralytics Metrics documentation.
keywords: YOLOv5, metrics, losses, confusion matrix, detection metrics, pose metrics, classification metrics, intersection over area, intersection over union, keypoint intersection over union, average precision, per class average precision, Ultralytics Docs
---
# FocalLoss
@ -95,4 +96,4 @@ description: Explore Ultralytics YOLO's FocalLoss, DetMetrics, PoseMetrics, Clas
# ap_per_class
---
:::ultralytics.yolo.utils.metrics.ap_per_class
<br><br>
<br><br>

3
docs/reference/yolo/utils/ops.md
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@ -1,5 +1,6 @@
---
description: Learn about various utility functions in Ultralytics YOLO, including x, y, width, height conversions, non-max suppression, and more.
keywords: Ultralytics, YOLO, Utils Ops, Functions, coco80_to_coco91_class, scale_boxes, non_max_suppression, clip_coords, xyxy2xywh, xywhn2xyxy, xyn2xy, xyxy2ltwh, ltwh2xyxy, resample_segments, process_mask_upsample, process_mask_native, masks2segments, clean_str
---
# Profile
@ -135,4 +136,4 @@ description: Learn about various utility functions in Ultralytics YOLO, includin
# clean_str
---
:::ultralytics.yolo.utils.ops.clean_str
<br><br>
<br><br>

3
docs/reference/yolo/utils/plotting.md
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@ -1,5 +1,6 @@
---
description: 'Discover the power of YOLO''s plotting functions: Colors, Labels and Images. Code examples to output targets and visualize features. Check it now.'
keywords: YOLO, object detection, plotting, visualization, annotator, save one box, plot results, feature visualization, Ultralytics
---
# Colors
@ -40,4 +41,4 @@ description: 'Discover the power of YOLO''s plotting functions: Colors, Labels a
# feature_visualization
---
:::ultralytics.yolo.utils.plotting.feature_visualization
<br><br>
<br><br>

3
docs/reference/yolo/utils/tal.md
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@ -1,5 +1,6 @@
---
description: Improve your YOLO models with Ultralytics' TaskAlignedAssigner, select_highest_overlaps, and dist2bbox utilities. Streamline your workflow today.
keywords: Ultrayltics, YOLO, select_candidates_in_gts, make_anchor, bbox2dist, object detection, tracking
---
# TaskAlignedAssigner
@ -30,4 +31,4 @@ description: Improve your YOLO models with Ultralytics' TaskAlignedAssigner, sel
# bbox2dist
---
:::ultralytics.yolo.utils.tal.bbox2dist
<br><br>
<br><br>

3
docs/reference/yolo/utils/torch_utils.md
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@ -1,5 +1,6 @@
---
description: Optimize your PyTorch models with Ultralytics YOLO's torch_utils functions such as ModelEMA, select_device, and is_parallel.
keywords: Ultralytics YOLO, Torch, Utils, Pytorch, Object Detection
---
# ModelEMA
@ -130,4 +131,4 @@ description: Optimize your PyTorch models with Ultralytics YOLO's torch_utils fu
# profile
---
:::ultralytics.yolo.utils.torch_utils.profile
<br><br>
<br><br>

3
docs/reference/yolo/v8/classify/predict.md
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@ -1,5 +1,6 @@
---
description: Learn how to use ClassificationPredictor in Ultralytics YOLOv8 for object classification tasks in a simple and efficient way.
keywords: Ultralytics, YOLO, v8, Classify Predictor, object detection, classification, computer vision
---
# ClassificationPredictor
@ -10,4 +11,4 @@ description: Learn how to use ClassificationPredictor in Ultralytics YOLOv8 for
# predict
---
:::ultralytics.yolo.v8.classify.predict.predict
<br><br>
<br><br>

3
docs/reference/yolo/v8/classify/train.md
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@ -1,5 +1,6 @@
---
description: Train a custom image classification model using Ultralytics YOLOv8 with ClassificationTrainer. Boost accuracy and efficiency today.
keywords: Ultralytics, YOLOv8, object detection, classification, training, API
---
# ClassificationTrainer
@ -10,4 +11,4 @@ description: Train a custom image classification model using Ultralytics YOLOv8
# train
---
:::ultralytics.yolo.v8.classify.train.train
<br><br>
<br><br>

3
docs/reference/yolo/v8/classify/val.md
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@ -1,5 +1,6 @@
---
description: Ensure model classification accuracy with Ultralytics YOLO's ClassificationValidator. Validate and improve your model with ease.
keywords: ClassificationValidator, Ultralytics YOLO, Validation, Data Science, Deep Learning
---
# ClassificationValidator
@ -10,4 +11,4 @@ description: Ensure model classification accuracy with Ultralytics YOLO's Classi
# val
---
:::ultralytics.yolo.v8.classify.val.val
<br><br>
<br><br>

3
docs/reference/yolo/v8/detect/predict.md
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@ -1,5 +1,6 @@
---
description: Detect and predict objects in images and videos using the Ultralytics YOLO v8 model with DetectionPredictor.
keywords: detectionpredictor, ultralytics yolo, object detection, neural network, machine learning
---
# DetectionPredictor
@ -10,4 +11,4 @@ description: Detect and predict objects in images and videos using the Ultralyti
# predict
---
:::ultralytics.yolo.v8.detect.predict.predict
<br><br>
<br><br>

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docs/reference/yolo/v8/detect/train.md
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@ -1,5 +1,6 @@
---
description: Train and optimize custom object detection models with Ultralytics DetectionTrainer and train functions. Get started with YOLO v8 today.
keywords: DetectionTrainer, Ultralytics YOLO, custom object detection, train models, AI applications
---
# DetectionTrainer
@ -10,4 +11,4 @@ description: Train and optimize custom object detection models with Ultralytics
# train
---
:::ultralytics.yolo.v8.detect.train.train
<br><br>
<br><br>

3
docs/reference/yolo/v8/detect/val.md
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@ -1,5 +1,6 @@
---
description: Validate YOLOv5 detections using this PyTorch module. Ensure model accuracy with NMS IOU threshold tuning and label mapping.
keywords: detection, validator, YOLOv5, object detection, model improvement, Ultralytics Docs
---
# DetectionValidator
@ -10,4 +11,4 @@ description: Validate YOLOv5 detections using this PyTorch module. Ensure model
# val
---
:::ultralytics.yolo.v8.detect.val.val
<br><br>
<br><br>

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docs/reference/yolo/v8/pose/predict.md
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---
description: Predict human pose coordinates and confidence scores using YOLOv5. Use on real-time video streams or static images.
keywords: Ultralytics, YOLO, v8, documentation, PosePredictor, pose prediction, pose estimation, predict method
---
# PosePredictor
@ -10,4 +11,4 @@ description: Predict human pose coordinates and confidence scores using YOLOv5.
# predict
---
:::ultralytics.yolo.v8.pose.predict.predict
<br><br>
<br><br>

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docs/reference/yolo/v8/pose/train.md
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@ -1,5 +1,6 @@
---
description: Boost posture detection using PoseTrainer and train models using train() API. Learn PoseLoss for ultra-fast and accurate pose detection with Ultralytics YOLO.
keywords: PoseTrainer, human pose models, deep learning, computer vision, Ultralytics YOLO, v8
---
# PoseTrainer
@ -10,4 +11,4 @@ description: Boost posture detection using PoseTrainer and train models using tr
# train
---
:::ultralytics.yolo.v8.pose.train.train
<br><br>
<br><br>

3
docs/reference/yolo/v8/pose/val.md
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@ -1,5 +1,6 @@
---
description: Ensure proper human poses in images with YOLOv8 Pose Validation, part of the Ultralytics YOLO v8 suite.
keywords: PoseValidator, Ultralytics YOLO, object detection, pose analysis, validation
---
# PoseValidator
@ -10,4 +11,4 @@ description: Ensure proper human poses in images with YOLOv8 Pose Validation, pa
# val
---
:::ultralytics.yolo.v8.pose.val.val
<br><br>
<br><br>

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docs/reference/yolo/v8/segment/predict.md
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@ -1,5 +1,6 @@
---
description: '"Use SegmentationPredictor in YOLOv8 for efficient object detection and segmentation. Explore Ultralytics YOLO Docs for more information."'
keywords: Ultralytics YOLO, SegmentationPredictor, object detection, segmentation masks, predict
---
# SegmentationPredictor
@ -10,4 +11,4 @@ description: '"Use SegmentationPredictor in YOLOv8 for efficient object detectio
# predict
---
:::ultralytics.yolo.v8.segment.predict.predict
<br><br>
<br><br>

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docs/reference/yolo/v8/segment/train.md
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---
description: Learn about SegmentationTrainer and Train in Ultralytics YOLO v8 for efficient object detection models. Improve your training with Ultralytics Docs.
keywords: SegmentationTrainer, Ultralytics YOLO, object detection, segmentation, train, tutorial, guide, code examples
---
# SegmentationTrainer
@ -10,4 +11,4 @@ description: Learn about SegmentationTrainer and Train in Ultralytics YOLO v8 fo
# train
---
:::ultralytics.yolo.v8.segment.train.train
<br><br>
<br><br>

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docs/reference/yolo/v8/segment/val.md
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---
description: Ensure segmentation quality on large datasets with SegmentationValidator. Review and visualize results with ease. Learn more at Ultralytics Docs.
keywords: SegmentationValidator, YOLOv8, Ultralytics Docs, segmentation model, validation
---
# SegmentationValidator
@ -10,4 +11,4 @@ description: Ensure segmentation quality on large datasets with SegmentationVali
# val
---
:::ultralytics.yolo.v8.segment.val.val
<br><br>
<br><br>