Docs updates for HUB, YOLOv4, YOLOv7, NAS (#3174)
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
GitHub
@ -1,5 +1,6 @@
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---
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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.
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keywords: YOLOv5, batch size, training, Ultralytics Autobatch, object detection, model performance
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---
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# check_train_batch_size
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@ -10,4 +11,4 @@ description: Dynamically adjusts input size to optimize GPU memory usage during
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# autobatch
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---
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:::ultralytics.yolo.utils.autobatch.autobatch
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<br><br>
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<br><br>
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@ -1,5 +1,6 @@
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---
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description: Improve your YOLO's performance and measure its speed. Benchmark utility for YOLOv5.
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keywords: Ultralytics YOLO, ProfileModels, benchmark, model inference, detection
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---
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# ProfileModels
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@ -10,4 +11,4 @@ description: Improve your YOLO's performance and measure its speed. Benchmark ut
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# benchmark
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---
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:::ultralytics.yolo.utils.benchmarks.benchmark
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<br><br>
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<br><br>
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---
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description: Learn about YOLO's callback functions from on_train_start to add_integration_callbacks. See how these callbacks modify and save models.
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keywords: YOLO, Ultralytics, callbacks, object detection, training, inference
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---
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# on_pretrain_routine_start
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@ -135,4 +136,4 @@ description: Learn about YOLO's callback functions from on_train_start to add_in
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# add_integration_callbacks
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---
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:::ultralytics.yolo.utils.callbacks.base.add_integration_callbacks
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<br><br>
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<br><br>
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@ -1,5 +1,6 @@
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---
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description: Improve your YOLOv5 model training with callbacks from ClearML. Learn about log debug samples, pre-training routines, validation and more.
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keywords: Ultralytics YOLO, callbacks, log plots, epoch monitoring, training end events
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---
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# _log_debug_samples
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@ -35,4 +36,4 @@ description: Improve your YOLOv5 model training with callbacks from ClearML. Lea
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# on_train_end
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---
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:::ultralytics.yolo.utils.callbacks.clearml.on_train_end
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<br><br>
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<br><br>
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@ -1,5 +1,6 @@
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---
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description: Learn about YOLO callbacks using the Comet.ml platform, enhancing object detection training and testing with custom logging and visualizations.
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keywords: Ultralytics, YOLO, callbacks, Comet ML, log images, log predictions, log plots, fetch metadata, fetch annotations, create experiment data, format experiment data
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---
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# _get_comet_mode
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@ -120,4 +121,4 @@ description: Learn about YOLO callbacks using the Comet.ml platform, enhancing o
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# on_train_end
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---
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:::ultralytics.yolo.utils.callbacks.comet.on_train_end
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<br><br>
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<br><br>
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54
docs/reference/yolo/utils/callbacks/dvc.md
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54
docs/reference/yolo/utils/callbacks/dvc.md
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---
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description: Explore Ultralytics YOLO Utils DVC Callbacks such as logging images, plots, confusion matrices, and training progress.
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keywords: Ultralytics, YOLO, Utils, DVC, Callbacks, images, plots, confusion matrices, training progress
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---
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# _logger_disabled
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---
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:::ultralytics.yolo.utils.callbacks.dvc._logger_disabled
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<br><br>
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# _log_images
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---
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:::ultralytics.yolo.utils.callbacks.dvc._log_images
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<br><br>
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# _log_plots
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---
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:::ultralytics.yolo.utils.callbacks.dvc._log_plots
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<br><br>
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# _log_confusion_matrix
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---
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:::ultralytics.yolo.utils.callbacks.dvc._log_confusion_matrix
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<br><br>
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# on_pretrain_routine_start
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---
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:::ultralytics.yolo.utils.callbacks.dvc.on_pretrain_routine_start
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<br><br>
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# on_pretrain_routine_end
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---
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:::ultralytics.yolo.utils.callbacks.dvc.on_pretrain_routine_end
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<br><br>
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# on_train_start
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---
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:::ultralytics.yolo.utils.callbacks.dvc.on_train_start
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<br><br>
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# on_train_epoch_start
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---
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:::ultralytics.yolo.utils.callbacks.dvc.on_train_epoch_start
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<br><br>
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# on_fit_epoch_end
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---
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:::ultralytics.yolo.utils.callbacks.dvc.on_fit_epoch_end
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<br><br>
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# on_train_end
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---
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:::ultralytics.yolo.utils.callbacks.dvc.on_train_end
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<br><br>
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---
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description: Improve YOLOv5 model training with Ultralytics' on-train callbacks. Boost performance on-pretrain-routine-end, model-save, train/predict start.
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keywords: Ultralytics, YOLO, callbacks, on_pretrain_routine_end, on_fit_epoch_end, on_train_start, on_val_start, on_predict_start, on_export_start
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---
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# on_pretrain_routine_end
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@ -40,4 +41,4 @@ description: Improve YOLOv5 model training with Ultralytics' on-train callbacks.
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# on_export_start
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---
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:::ultralytics.yolo.utils.callbacks.hub.on_export_start
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<br><br>
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<br><br>
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---
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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.
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keywords: Ultralytics, YOLO, Utils, MLflow, callbacks, on_pretrain_routine_end, on_train_end, Tracking, Model Management, training
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---
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# on_pretrain_routine_end
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@ -15,4 +16,4 @@ description: Track model performance and metrics with MLflow in YOLOv5. Use call
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# on_train_end
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---
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:::ultralytics.yolo.utils.callbacks.mlflow.on_train_end
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<br><br>
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<br><br>
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---
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description: Improve YOLOv5 training with Neptune, a powerful logging tool. Track metrics like images, plots, and epochs for better model performance.
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keywords: Ultralytics, YOLO, Neptune, Callbacks, log scalars, log images, log plots, training, validation
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---
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# _log_scalars
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@ -40,4 +41,4 @@ description: Improve YOLOv5 training with Neptune, a powerful logging tool. Trac
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# on_train_end
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---
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:::ultralytics.yolo.utils.callbacks.neptune.on_train_end
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<br><br>
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<br><br>
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@ -1,8 +1,9 @@
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---
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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."'
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keywords: on_fit_epoch_end, Ultralytics YOLO, callback function, training, model tuning
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---
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# on_fit_epoch_end
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---
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:::ultralytics.yolo.utils.callbacks.raytune.on_fit_epoch_end
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<br><br>
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<br><br>
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---
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description: Learn how to monitor the training process with Tensorboard using Ultralytics YOLO's "_log_scalars" and "on_batch_end" methods.
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keywords: TensorBoard callbacks, YOLO training, ultralytics YOLO
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---
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# _log_scalars
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@ -20,4 +21,4 @@ description: Learn how to monitor the training process with Tensorboard using Ul
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# on_fit_epoch_end
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---
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:::ultralytics.yolo.utils.callbacks.tensorboard.on_fit_epoch_end
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<br><br>
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<br><br>
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@ -1,7 +1,13 @@
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---
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description: Learn how to use Ultralytics YOLO's built-in callbacks `on_pretrain_routine_start` and `on_train_epoch_end` for improved training performance.
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keywords: Ultralytics, YOLO, callbacks, weights, biases, training
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---
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# _log_plots
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---
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:::ultralytics.yolo.utils.callbacks.wb._log_plots
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<br><br>
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# on_pretrain_routine_start
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---
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:::ultralytics.yolo.utils.callbacks.wb.on_pretrain_routine_start
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@ -20,4 +26,4 @@ description: Learn how to use Ultralytics YOLO's built-in callbacks `on_pretrain
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# on_train_end
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---
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:::ultralytics.yolo.utils.callbacks.wb.on_train_end
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<br><br>
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<br><br>
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---
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description: 'Check functions for YOLO utils: image size, version, font, requirements, filename suffix, YAML file, YOLO, and Git version.'
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keywords: YOLO, Ultralytics, Utils, Checks, image sizing, version updates, font compatibility, Python requirements, file suffixes, YAML syntax, image showing, AMP
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---
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# is_ascii
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@ -72,6 +73,11 @@ description: 'Check functions for YOLO utils: image size, version, font, require
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:::ultralytics.yolo.utils.checks.check_yolo
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<br><br>
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# check_amp
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---
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:::ultralytics.yolo.utils.checks.check_amp
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<br><br>
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# git_describe
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---
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:::ultralytics.yolo.utils.checks.git_describe
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@ -80,4 +86,4 @@ description: 'Check functions for YOLO utils: image size, version, font, require
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# print_args
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---
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:::ultralytics.yolo.utils.checks.print_args
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<br><br>
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<br><br>
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---
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description: Learn how to find free network port and generate DDP (Distributed Data Parallel) command in Ultralytics YOLO with easy examples.
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keywords: ultralytics, YOLO, utils, dist, distributed deep learning, DDP file, DDP cleanup
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---
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# find_free_network_port
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@ -20,4 +21,4 @@ description: Learn how to find free network port and generate DDP (Distributed D
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# ddp_cleanup
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---
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:::ultralytics.yolo.utils.dist.ddp_cleanup
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<br><br>
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<br><br>
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---
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description: Download and unzip YOLO pretrained models. Ultralytics YOLO docs utils.downloads.unzip_file, checks disk space, downloads and attempts assets.
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keywords: Ultralytics YOLO, downloads, trained models, datasets, weights, deep learning, computer vision
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---
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# is_url
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@ -30,4 +31,4 @@ description: Download and unzip YOLO pretrained models. Ultralytics YOLO docs ut
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# download
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---
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:::ultralytics.yolo.utils.downloads.download
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<br><br>
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<br><br>
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---
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description: Learn about HUBModelError in Ultralytics YOLO Docs. Resolve the error and get the most out of your YOLO model.
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keywords: HUBModelError, Ultralytics YOLO, YOLO Documentation, Object detection errors, YOLO Errors, HUBModelError Solutions
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---
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# HUBModelError
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---
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:::ultralytics.yolo.utils.errors.HUBModelError
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<br><br>
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<br><br>
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---
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description: 'Learn about Ultralytics YOLO files and directory utilities: WorkingDirectory, file_age, file_size, and make_dirs.'
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keywords: YOLO, object detection, file utils, file age, file size, working directory, make directories, Ultralytics Docs
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---
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# WorkingDirectory
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# make_dirs
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---
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:::ultralytics.yolo.utils.files.make_dirs
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<br><br>
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<br><br>
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---
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description: Learn about Bounding Boxes (Bboxes) and _ntuple in Ultralytics YOLO for object detection. Improve accuracy and speed with these powerful tools.
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keywords: Ultralytics, YOLO, Bboxes, _ntuple, object detection, instance segmentation
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---
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# Bboxes
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# _ntuple
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---
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:::ultralytics.yolo.utils.instance._ntuple
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<br><br>
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<br><br>
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@ -1,5 +1,6 @@
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---
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description: Learn about Varifocal Loss and Keypoint Loss in Ultralytics YOLO for advanced bounding box and pose estimation. Visit our docs for more.
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keywords: Ultralytics, YOLO, loss functions, object detection, keypoint detection, segmentation, classification
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---
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# VarifocalLoss
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# v8ClassificationLoss
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---
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:::ultralytics.yolo.utils.loss.v8ClassificationLoss
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<br><br>
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<br><br>
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---
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description: Explore Ultralytics YOLO's FocalLoss, DetMetrics, PoseMetrics, ClassifyMetrics, and more with Ultralytics Metrics documentation.
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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
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---
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# FocalLoss
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# ap_per_class
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---
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:::ultralytics.yolo.utils.metrics.ap_per_class
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<br><br>
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<br><br>
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---
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description: Learn about various utility functions in Ultralytics YOLO, including x, y, width, height conversions, non-max suppression, and more.
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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
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---
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# Profile
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@ -135,4 +136,4 @@ description: Learn about various utility functions in Ultralytics YOLO, includin
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# clean_str
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---
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:::ultralytics.yolo.utils.ops.clean_str
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<br><br>
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<br><br>
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@ -1,5 +1,6 @@
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---
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description: 'Discover the power of YOLO''s plotting functions: Colors, Labels and Images. Code examples to output targets and visualize features. Check it now.'
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keywords: YOLO, object detection, plotting, visualization, annotator, save one box, plot results, feature visualization, Ultralytics
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---
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# Colors
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# feature_visualization
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---
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:::ultralytics.yolo.utils.plotting.feature_visualization
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<br><br>
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<br><br>
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@ -1,5 +1,6 @@
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---
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description: Improve your YOLO models with Ultralytics' TaskAlignedAssigner, select_highest_overlaps, and dist2bbox utilities. Streamline your workflow today.
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keywords: Ultrayltics, YOLO, select_candidates_in_gts, make_anchor, bbox2dist, object detection, tracking
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---
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# TaskAlignedAssigner
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# bbox2dist
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---
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:::ultralytics.yolo.utils.tal.bbox2dist
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<br><br>
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<br><br>
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---
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description: Optimize your PyTorch models with Ultralytics YOLO's torch_utils functions such as ModelEMA, select_device, and is_parallel.
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keywords: Ultralytics YOLO, Torch, Utils, Pytorch, Object Detection
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---
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# ModelEMA
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@ -130,4 +131,4 @@ description: Optimize your PyTorch models with Ultralytics YOLO's torch_utils fu
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# profile
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---
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:::ultralytics.yolo.utils.torch_utils.profile
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<br><br>
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<br><br>
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