ultralytics 8.0.97 confusion matrix, windows, docs updates (#2511)
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com> Co-authored-by: Dowon <ks2515@naver.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -1,4 +1,8 @@
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---
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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.
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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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<br><br>
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<br><br>
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---
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description: Accelerate your AI development with the Ultralytics HUB Training Session. High-performance training of object detection models.
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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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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: Explore Ultralytics events, including 'request_with_credentials' and 'smart_request', to improve your project's performance and efficiency.
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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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@ -16,4 +20,4 @@
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# smart_request
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---
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:::ultralytics.hub.utils.smart_request
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: Ensure class names match filenames for easy imports. Use AutoBackend to automatically rename and refactor model files.
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---
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# AutoBackend
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---
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:::ultralytics.nn.autobackend.AutoBackend
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@ -6,4 +10,4 @@
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# check_class_names
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---
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:::ultralytics.nn.autobackend.check_class_names
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: Detect 80+ object categories with bounding box coordinates and class probabilities using AutoShape in Ultralytics YOLO. Explore Detections now.
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---
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# AutoShape
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---
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:::ultralytics.nn.autoshape.AutoShape
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@ -6,4 +10,4 @@
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# Detections
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---
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:::ultralytics.nn.autoshape.Detections
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: Explore Ultralytics neural network modules for convolution, attention, detection, pose, and classification in PyTorch.
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---
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# Conv
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---
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:::ultralytics.nn.modules.Conv
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@ -166,4 +170,4 @@
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# autopad
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---
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:::ultralytics.nn.modules.autopad
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: Learn how to work with Ultralytics YOLO Detection, Segmentation & Classification Models, load weights and parse models in PyTorch.
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---
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# BaseModel
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---
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:::ultralytics.nn.tasks.BaseModel
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@ -56,4 +60,4 @@
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# guess_model_task
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---
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:::ultralytics.nn.tasks.guess_model_task
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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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.
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---
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# on_predict_start
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---
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:::ultralytics.tracker.track.on_predict_start
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# register_tracker
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---
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:::ultralytics.tracker.track.register_tracker
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: 'TrackState: A comprehensive guide to Ultralytics tracker''s BaseTrack for monitoring model performance. Improve your tracking capabilities now!'
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---
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# TrackState
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---
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:::ultralytics.tracker.trackers.basetrack.TrackState
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@ -6,4 +10,4 @@
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# BaseTrack
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---
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:::ultralytics.tracker.trackers.basetrack.BaseTrack
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: '"Optimize tracking with Ultralytics BOTrack. Easily sort and track bots with BOTSORT. Streamline data collection for improved performance."'
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---
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# BOTrack
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---
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:::ultralytics.tracker.trackers.bot_sort.BOTrack
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@ -6,4 +10,4 @@
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# BOTSORT
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---
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:::ultralytics.tracker.trackers.bot_sort.BOTSORT
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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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.
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---
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# STrack
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---
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:::ultralytics.tracker.trackers.byte_tracker.STrack
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@ -6,4 +10,4 @@
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# BYTETracker
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---
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:::ultralytics.tracker.trackers.byte_tracker.BYTETracker
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<br><br>
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<br><br>
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@ -1,4 +1,8 @@
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---
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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."'
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---
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# GMC
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---
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:::ultralytics.tracker.utils.gmc.GMC
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: Improve object tracking with KalmanFilterXYAH in Ultralytics YOLO - an efficient and accurate algorithm for state estimation.
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---
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# KalmanFilterXYAH
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---
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:::ultralytics.tracker.utils.kalman_filter.KalmanFilterXYAH
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@ -6,4 +10,4 @@
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# KalmanFilterXYWH
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---
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:::ultralytics.tracker.utils.kalman_filter.KalmanFilterXYWH
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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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.
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---
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# merge_matches
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---
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:::ultralytics.tracker.utils.matching.merge_matches
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@ -56,4 +60,4 @@
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# bbox_ious
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---
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:::ultralytics.tracker.utils.matching.bbox_ious
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<br><br>
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<br><br>
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@ -1,4 +1,8 @@
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---
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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.
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---
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# auto_annotate
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---
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:::ultralytics.yolo.data.annotator.auto_annotate
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: Use Ultralytics YOLO Data Augmentation transforms with Base, MixUp, and Albumentations for object detection and classification.
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---
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# BaseTransform
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---
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:::ultralytics.yolo.data.augment.BaseTransform
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@ -86,4 +90,4 @@
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# classify_albumentations
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---
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:::ultralytics.yolo.data.augment.classify_albumentations
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<br><br>
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<br><br>
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@ -1,4 +1,8 @@
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---
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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.
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---
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# BaseDataset
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---
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:::ultralytics.yolo.data.base.BaseDataset
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: Maximize YOLO performance with Ultralytics' InfiniteDataLoader, seed_worker, build_dataloader, and load_inference_source functions.
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---
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# InfiniteDataLoader
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---
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:::ultralytics.yolo.data.build.InfiniteDataLoader
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@ -31,4 +35,4 @@
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# load_inference_source
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---
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:::ultralytics.yolo.data.build.load_inference_source
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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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.
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---
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# coco91_to_coco80_class
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---
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:::ultralytics.yolo.data.converter.coco91_to_coco80_class
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@ -26,4 +30,4 @@
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# delete_dsstore
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---
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:::ultralytics.yolo.data.converter.delete_dsstore
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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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.'
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---
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# SourceTypes
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---
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:::ultralytics.yolo.data.dataloaders.stream_loaders.SourceTypes
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@ -31,4 +35,4 @@
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# autocast_list
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---
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:::ultralytics.yolo.data.dataloaders.stream_loaders.autocast_list
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: Enhance image data with Albumentations CenterCrop, normalize, augment_hsv, replicate, random_perspective, cutout, & box_candidates.
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---
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# Albumentations
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---
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:::ultralytics.yolo.data.dataloaders.v5augmentations.Albumentations
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@ -81,4 +85,4 @@
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# classify_transforms
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---
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:::ultralytics.yolo.data.dataloaders.v5augmentations.classify_transforms
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: Efficiently load images and labels to models using Ultralytics YOLO's InfiniteDataLoader, LoadScreenshots, and LoadStreams.
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---
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# InfiniteDataLoader
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---
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:::ultralytics.yolo.data.dataloaders.v5loader.InfiniteDataLoader
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# create_classification_dataloader
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---
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:::ultralytics.yolo.data.dataloaders.v5loader.create_classification_dataloader
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: Create custom YOLOv5 datasets with Ultralytics YOLODataset and SemanticDataset. Streamline your object detection and segmentation projects.
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---
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# YOLODataset
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---
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:::ultralytics.yolo.data.dataset.YOLODataset
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# SemanticDataset
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---
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:::ultralytics.yolo.data.dataset.SemanticDataset
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<br><br>
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<br><br>
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@ -1,4 +1,8 @@
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---
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description: Create a custom dataset of mixed and oriented rectangular objects with Ultralytics YOLO's MixAndRectDataset.
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---
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# MixAndRectDataset
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---
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:::ultralytics.yolo.data.dataset_wrappers.MixAndRectDataset
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: Efficiently handle data in YOLO with Ultralytics. Utilize HUBDatasetStats and customize dataset with these data utility functions.
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---
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# HUBDatasetStats
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---
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:::ultralytics.yolo.data.utils.HUBDatasetStats
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# zip_directory
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---
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:::ultralytics.yolo.data.utils.zip_directory
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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description: Learn how to export your YOLO model in various formats using Ultralytics' exporter package - iOS, GDC, and more.
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---
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# Exporter
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---
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:::ultralytics.yolo.engine.exporter.Exporter
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# export
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---
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:::ultralytics.yolo.engine.exporter.export
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<br><br>
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<br><br>
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@ -1,4 +1,8 @@
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---
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description: Discover the YOLO model of Ultralytics engine to simplify your object detection tasks with state-of-the-art models.
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---
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# YOLO
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---
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:::ultralytics.yolo.engine.model.YOLO
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<br><br>
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<br><br>
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@ -1,4 +1,8 @@
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---
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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."'
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---
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# BasePredictor
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---
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:::ultralytics.yolo.engine.predictor.BasePredictor
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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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.
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---
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# BaseTensor
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---
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:::ultralytics.yolo.engine.results.BaseTensor
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@ -16,4 +20,4 @@
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# Masks
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---
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:::ultralytics.yolo.engine.results.Masks
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<br><br>
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<br><br>
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@ -1,3 +1,7 @@
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---
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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.
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---
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# BaseTrainer
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---
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:::ultralytics.yolo.engine.trainer.BaseTrainer
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# check_amp
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---
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:::ultralytics.yolo.engine.trainer.check_amp
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<br><br>
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<br><br>
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@ -1,4 +1,8 @@
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---
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description: Ensure YOLOv5 models meet constraints and standards with the BaseValidator class. Learn how to use it here.
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---
|
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# BaseValidator
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---
|
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:::ultralytics.yolo.engine.validator.BaseValidator
|
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<br><br>
|
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<br><br>
|
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@ -1,3 +1,7 @@
|
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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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---
|
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# check_train_batch_size
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---
|
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:::ultralytics.yolo.utils.autobatch.check_train_batch_size
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@ -6,4 +10,4 @@
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# autobatch
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---
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:::ultralytics.yolo.utils.autobatch.autobatch
|
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<br><br>
|
||||
<br><br>
|
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@ -1,4 +1,8 @@
|
||||
---
|
||||
description: Improve your YOLO's performance and measure its speed. Benchmark utility for YOLOv5.
|
||||
---
|
||||
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# benchmark
|
||||
---
|
||||
:::ultralytics.yolo.utils.benchmarks.benchmark
|
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<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
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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# on_pretrain_routine_start
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.base.on_pretrain_routine_start
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@ -131,4 +135,4 @@
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# add_integration_callbacks
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---
|
||||
:::ultralytics.yolo.utils.callbacks.base.add_integration_callbacks
|
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<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
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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# _log_debug_samples
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.clearml._log_debug_samples
|
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@ -31,4 +35,4 @@
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# on_train_end
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.clearml.on_train_end
|
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<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
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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# _get_comet_mode
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.comet._get_comet_mode
|
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@ -116,4 +120,4 @@
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# on_train_end
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.comet.on_train_end
|
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<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
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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.
|
||||
---
|
||||
|
||||
# on_pretrain_routine_end
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.hub.on_pretrain_routine_end
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@ -36,4 +40,4 @@
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# on_export_start
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.hub.on_export_start
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Track model performance and metrics with MLflow in YOLOv5. Use callbacks like on_pretrain_routine_end or on_train_end to log information.
|
||||
---
|
||||
|
||||
# on_pretrain_routine_end
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.mlflow.on_pretrain_routine_end
|
||||
@ -11,4 +15,4 @@
|
||||
# on_train_end
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.mlflow.on_train_end
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Improve YOLOv5 training with Neptune, a powerful logging tool. Track metrics like images, plots, and epochs for better model performance.
|
||||
---
|
||||
|
||||
# _log_scalars
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.neptune._log_scalars
|
||||
@ -36,4 +40,4 @@
|
||||
# on_train_end
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.neptune.on_train_end
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,4 +1,8 @@
|
||||
---
|
||||
description: '"Improve YOLO model performance with on_fit_epoch_end callback. Learn to integrate with Ray Tune for hyperparameter tuning. Ultralytics YOLO docs."'
|
||||
---
|
||||
|
||||
# on_fit_epoch_end
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.raytune.on_fit_epoch_end
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Learn how to monitor the training process with Tensorboard using Ultralytics YOLO's "_log_scalars" and "on_batch_end" methods.
|
||||
---
|
||||
|
||||
# _log_scalars
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.tensorboard._log_scalars
|
||||
@ -16,4 +20,4 @@
|
||||
# on_fit_epoch_end
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.tensorboard.on_fit_epoch_end
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Learn how to use Ultralytics YOLO's built-in callbacks `on_pretrain_routine_start` and `on_train_epoch_end` for improved training performance.
|
||||
---
|
||||
|
||||
# on_pretrain_routine_start
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.wb.on_pretrain_routine_start
|
||||
@ -16,4 +20,4 @@
|
||||
# on_train_end
|
||||
---
|
||||
:::ultralytics.yolo.utils.callbacks.wb.on_train_end
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: 'Check functions for YOLO utils: image size, version, font, requirements, filename suffix, YAML file, YOLO, and Git version.'
|
||||
---
|
||||
|
||||
# is_ascii
|
||||
---
|
||||
:::ultralytics.yolo.utils.checks.is_ascii
|
||||
@ -76,4 +80,4 @@
|
||||
# print_args
|
||||
---
|
||||
:::ultralytics.yolo.utils.checks.print_args
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Learn how to find free network port and generate DDP (Distributed Data Parallel) command in Ultralytics YOLO with easy examples.
|
||||
---
|
||||
|
||||
# find_free_network_port
|
||||
---
|
||||
:::ultralytics.yolo.utils.dist.find_free_network_port
|
||||
@ -16,4 +20,4 @@
|
||||
# ddp_cleanup
|
||||
---
|
||||
:::ultralytics.yolo.utils.dist.ddp_cleanup
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Download and unzip YOLO pretrained models. Ultralytics YOLO docs utils.downloads.unzip_file, checks disk space, downloads and attempts assets.
|
||||
---
|
||||
|
||||
# is_url
|
||||
---
|
||||
:::ultralytics.yolo.utils.downloads.is_url
|
||||
@ -26,4 +30,4 @@
|
||||
# download
|
||||
---
|
||||
:::ultralytics.yolo.utils.downloads.download
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,4 +1,8 @@
|
||||
---
|
||||
description: Learn about HUBModelError in Ultralytics YOLO Docs. Resolve the error and get the most out of your YOLO model.
|
||||
---
|
||||
|
||||
# HUBModelError
|
||||
---
|
||||
:::ultralytics.yolo.utils.errors.HUBModelError
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: 'Learn about Ultralytics YOLO files and directory utilities: WorkingDirectory, file_age, file_size, and make_dirs.'
|
||||
---
|
||||
|
||||
# WorkingDirectory
|
||||
---
|
||||
:::ultralytics.yolo.utils.files.WorkingDirectory
|
||||
@ -31,4 +35,4 @@
|
||||
# make_dirs
|
||||
---
|
||||
:::ultralytics.yolo.utils.files.make_dirs
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Learn about Bounding Boxes (Bboxes) and _ntuple in Ultralytics YOLO for object detection. Improve accuracy and speed with these powerful tools.
|
||||
---
|
||||
|
||||
# Bboxes
|
||||
---
|
||||
:::ultralytics.yolo.utils.instance.Bboxes
|
||||
@ -11,4 +15,4 @@
|
||||
# _ntuple
|
||||
---
|
||||
:::ultralytics.yolo.utils.instance._ntuple
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Learn about Varifocal Loss and Keypoint Loss in Ultralytics YOLO for advanced bounding box and pose estimation. Visit our docs for more.
|
||||
---
|
||||
|
||||
# VarifocalLoss
|
||||
---
|
||||
:::ultralytics.yolo.utils.loss.VarifocalLoss
|
||||
@ -11,4 +15,4 @@
|
||||
# KeypointLoss
|
||||
---
|
||||
:::ultralytics.yolo.utils.loss.KeypointLoss
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Explore Ultralytics YOLO's FocalLoss, DetMetrics, PoseMetrics, ClassifyMetrics, and more with Ultralytics Metrics documentation.
|
||||
---
|
||||
|
||||
# FocalLoss
|
||||
---
|
||||
:::ultralytics.yolo.utils.metrics.FocalLoss
|
||||
@ -91,4 +95,4 @@
|
||||
# ap_per_class
|
||||
---
|
||||
:::ultralytics.yolo.utils.metrics.ap_per_class
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Learn about various utility functions in Ultralytics YOLO, including x, y, width, height conversions, non-max suppression, and more.
|
||||
---
|
||||
|
||||
# Profile
|
||||
---
|
||||
:::ultralytics.yolo.utils.ops.Profile
|
||||
@ -131,4 +135,4 @@
|
||||
# clean_str
|
||||
---
|
||||
:::ultralytics.yolo.utils.ops.clean_str
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: 'Discover the power of YOLO''s plotting functions: Colors, Labels and Images. Code examples to output targets and visualize features. Check it now.'
|
||||
---
|
||||
|
||||
# Colors
|
||||
---
|
||||
:::ultralytics.yolo.utils.plotting.Colors
|
||||
@ -36,4 +40,4 @@
|
||||
# feature_visualization
|
||||
---
|
||||
:::ultralytics.yolo.utils.plotting.feature_visualization
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Improve your YOLO models with Ultralytics' TaskAlignedAssigner, select_highest_overlaps, and dist2bbox utilities. Streamline your workflow today.
|
||||
---
|
||||
|
||||
# TaskAlignedAssigner
|
||||
---
|
||||
:::ultralytics.yolo.utils.tal.TaskAlignedAssigner
|
||||
@ -26,4 +30,4 @@
|
||||
# bbox2dist
|
||||
---
|
||||
:::ultralytics.yolo.utils.tal.bbox2dist
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Optimize your PyTorch models with Ultralytics YOLO's torch_utils functions such as ModelEMA, select_device, and is_parallel.
|
||||
---
|
||||
|
||||
# ModelEMA
|
||||
---
|
||||
:::ultralytics.yolo.utils.torch_utils.ModelEMA
|
||||
@ -116,4 +120,4 @@
|
||||
# profile
|
||||
---
|
||||
:::ultralytics.yolo.utils.torch_utils.profile
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Learn how to use ClassificationPredictor in Ultralytics YOLOv8 for object classification tasks in a simple and efficient way.
|
||||
---
|
||||
|
||||
# ClassificationPredictor
|
||||
---
|
||||
:::ultralytics.yolo.v8.classify.predict.ClassificationPredictor
|
||||
@ -6,4 +10,4 @@
|
||||
# predict
|
||||
---
|
||||
:::ultralytics.yolo.v8.classify.predict.predict
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Train a custom image classification model using Ultralytics YOLOv8 with ClassificationTrainer. Boost accuracy and efficiency today.
|
||||
---
|
||||
|
||||
# ClassificationTrainer
|
||||
---
|
||||
:::ultralytics.yolo.v8.classify.train.ClassificationTrainer
|
||||
@ -6,4 +10,4 @@
|
||||
# train
|
||||
---
|
||||
:::ultralytics.yolo.v8.classify.train.train
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Ensure model classification accuracy with Ultralytics YOLO's ClassificationValidator. Validate and improve your model with ease.
|
||||
---
|
||||
|
||||
# ClassificationValidator
|
||||
---
|
||||
:::ultralytics.yolo.v8.classify.val.ClassificationValidator
|
||||
@ -6,4 +10,4 @@
|
||||
# val
|
||||
---
|
||||
:::ultralytics.yolo.v8.classify.val.val
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Detect and predict objects in images and videos using the Ultralytics YOLO v8 model with DetectionPredictor.
|
||||
---
|
||||
|
||||
# DetectionPredictor
|
||||
---
|
||||
:::ultralytics.yolo.v8.detect.predict.DetectionPredictor
|
||||
@ -6,4 +10,4 @@
|
||||
# predict
|
||||
---
|
||||
:::ultralytics.yolo.v8.detect.predict.predict
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Train and optimize custom object detection models with Ultralytics DetectionTrainer and train functions. Get started with YOLO v8 today.
|
||||
---
|
||||
|
||||
# DetectionTrainer
|
||||
---
|
||||
:::ultralytics.yolo.v8.detect.train.DetectionTrainer
|
||||
@ -11,4 +15,4 @@
|
||||
# train
|
||||
---
|
||||
:::ultralytics.yolo.v8.detect.train.train
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Validate YOLOv5 detections using this PyTorch module. Ensure model accuracy with NMS IOU threshold tuning and label mapping.
|
||||
---
|
||||
|
||||
# DetectionValidator
|
||||
---
|
||||
:::ultralytics.yolo.v8.detect.val.DetectionValidator
|
||||
@ -6,4 +10,4 @@
|
||||
# val
|
||||
---
|
||||
:::ultralytics.yolo.v8.detect.val.val
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Predict human pose coordinates and confidence scores using YOLOv5. Use on real-time video streams or static images.
|
||||
---
|
||||
|
||||
# PosePredictor
|
||||
---
|
||||
:::ultralytics.yolo.v8.pose.predict.PosePredictor
|
||||
@ -6,4 +10,4 @@
|
||||
# predict
|
||||
---
|
||||
:::ultralytics.yolo.v8.pose.predict.predict
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Boost posture detection using PoseTrainer and train models using train() API. Learn PoseLoss for ultra-fast and accurate pose detection with Ultralytics YOLO.
|
||||
---
|
||||
|
||||
# PoseTrainer
|
||||
---
|
||||
:::ultralytics.yolo.v8.pose.train.PoseTrainer
|
||||
@ -11,4 +15,4 @@
|
||||
# train
|
||||
---
|
||||
:::ultralytics.yolo.v8.pose.train.train
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Ensure proper human poses in images with YOLOv8 Pose Validation, part of the Ultralytics YOLO v8 suite.
|
||||
---
|
||||
|
||||
# PoseValidator
|
||||
---
|
||||
:::ultralytics.yolo.v8.pose.val.PoseValidator
|
||||
@ -6,4 +10,4 @@
|
||||
# val
|
||||
---
|
||||
:::ultralytics.yolo.v8.pose.val.val
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: '"Use SegmentationPredictor in YOLOv8 for efficient object detection and segmentation. Explore Ultralytics YOLO Docs for more information."'
|
||||
---
|
||||
|
||||
# SegmentationPredictor
|
||||
---
|
||||
:::ultralytics.yolo.v8.segment.predict.SegmentationPredictor
|
||||
@ -6,4 +10,4 @@
|
||||
# predict
|
||||
---
|
||||
:::ultralytics.yolo.v8.segment.predict.predict
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Learn about SegmentationTrainer and Train in Ultralytics YOLO v8 for efficient object detection models. Improve your training with Ultralytics Docs.
|
||||
---
|
||||
|
||||
# SegmentationTrainer
|
||||
---
|
||||
:::ultralytics.yolo.v8.segment.train.SegmentationTrainer
|
||||
@ -11,4 +15,4 @@
|
||||
# train
|
||||
---
|
||||
:::ultralytics.yolo.v8.segment.train.train
|
||||
<br><br>
|
||||
<br><br>
|
||||
@ -1,3 +1,7 @@
|
||||
---
|
||||
description: Ensure segmentation quality on large datasets with SegmentationValidator. Review and visualize results with ease. Learn more at Ultralytics Docs.
|
||||
---
|
||||
|
||||
# SegmentationValidator
|
||||
---
|
||||
:::ultralytics.yolo.v8.segment.val.SegmentationValidator
|
||||
@ -6,4 +10,4 @@
|
||||
# val
|
||||
---
|
||||
:::ultralytics.yolo.v8.segment.val.val
|
||||
<br><br>
|
||||
<br><br>
|
||||
Reference in New Issue
Block a user