ultralytics 8.0.105 classification hyp fix and new onplot callbacks (#2684)

Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ivan Shcheklein <shcheklein@gmail.com>
This commit is contained in:
Glenn Jocher
2023-05-17 19:10:20 +02:00
committed by GitHub
parent b1119d512e
commit 23fc50641c
92 changed files with 378 additions and 206 deletions

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@ -10,4 +10,4 @@ description: Dynamically adjusts input size to optimize GPU memory usage during
# autobatch
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:::ultralytics.yolo.utils.autobatch.autobatch
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@ -5,4 +5,4 @@ description: Improve your YOLO's performance and measure its speed. Benchmark ut
# benchmark
---
:::ultralytics.yolo.utils.benchmarks.benchmark
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@ -135,4 +135,4 @@ description: Learn about YOLO's callback functions from on_train_start to add_in
# add_integration_callbacks
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:::ultralytics.yolo.utils.callbacks.base.add_integration_callbacks
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@ -35,4 +35,4 @@ description: Improve your YOLOv5 model training with callbacks from ClearML. Lea
# on_train_end
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:::ultralytics.yolo.utils.callbacks.clearml.on_train_end
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@ -120,4 +120,4 @@ description: Learn about YOLO callbacks using the Comet.ml platform, enhancing o
# on_train_end
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:::ultralytics.yolo.utils.callbacks.comet.on_train_end
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@ -40,4 +40,4 @@ description: Improve YOLOv5 model training with Ultralytics' on-train callbacks.
# on_export_start
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:::ultralytics.yolo.utils.callbacks.hub.on_export_start
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@ -15,4 +15,4 @@ description: Track model performance and metrics with MLflow in YOLOv5. Use call
# on_train_end
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:::ultralytics.yolo.utils.callbacks.mlflow.on_train_end
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@ -40,4 +40,4 @@ description: Improve YOLOv5 training with Neptune, a powerful logging tool. Trac
# on_train_end
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:::ultralytics.yolo.utils.callbacks.neptune.on_train_end
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@ -5,4 +5,4 @@ description: '"Improve YOLO model performance with on_fit_epoch_end callback. Le
# on_fit_epoch_end
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:::ultralytics.yolo.utils.callbacks.raytune.on_fit_epoch_end
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@ -20,4 +20,4 @@ description: Learn how to monitor the training process with Tensorboard using Ul
# on_fit_epoch_end
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:::ultralytics.yolo.utils.callbacks.tensorboard.on_fit_epoch_end
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@ -20,4 +20,4 @@ description: Learn how to use Ultralytics YOLO's built-in callbacks `on_pretrain
# on_train_end
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:::ultralytics.yolo.utils.callbacks.wb.on_train_end
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@ -80,4 +80,4 @@ description: 'Check functions for YOLO utils: image size, version, font, require
# print_args
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:::ultralytics.yolo.utils.checks.print_args
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@ -20,4 +20,4 @@ description: Learn how to find free network port and generate DDP (Distributed D
# ddp_cleanup
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:::ultralytics.yolo.utils.dist.ddp_cleanup
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@ -30,4 +30,4 @@ description: Download and unzip YOLO pretrained models. Ultralytics YOLO docs ut
# download
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:::ultralytics.yolo.utils.downloads.download
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@ -5,4 +5,4 @@ description: Learn about HUBModelError in Ultralytics YOLO Docs. Resolve the err
# HUBModelError
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:::ultralytics.yolo.utils.errors.HUBModelError
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@ -35,4 +35,4 @@ description: 'Learn about Ultralytics YOLO files and directory utilities: Workin
# make_dirs
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:::ultralytics.yolo.utils.files.make_dirs
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@ -15,4 +15,4 @@ description: Learn about Bounding Boxes (Bboxes) and _ntuple in Ultralytics YOLO
# _ntuple
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:::ultralytics.yolo.utils.instance._ntuple
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@ -15,4 +15,4 @@ description: Learn about Varifocal Loss and Keypoint Loss in Ultralytics YOLO fo
# KeypointLoss
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:::ultralytics.yolo.utils.loss.KeypointLoss
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@ -95,4 +95,4 @@ description: Explore Ultralytics YOLO's FocalLoss, DetMetrics, PoseMetrics, Clas
# ap_per_class
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:::ultralytics.yolo.utils.metrics.ap_per_class
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@ -135,4 +135,4 @@ description: Learn about various utility functions in Ultralytics YOLO, includin
# clean_str
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:::ultralytics.yolo.utils.ops.clean_str
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@ -40,4 +40,4 @@ description: 'Discover the power of YOLO''s plotting functions: Colors, Labels a
# feature_visualization
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:::ultralytics.yolo.utils.plotting.feature_visualization
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@ -30,4 +30,4 @@ description: Improve your YOLO models with Ultralytics' TaskAlignedAssigner, sel
# bbox2dist
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:::ultralytics.yolo.utils.tal.bbox2dist
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@ -120,4 +120,4 @@ description: Optimize your PyTorch models with Ultralytics YOLO's torch_utils fu
# profile
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:::ultralytics.yolo.utils.torch_utils.profile
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