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ultralytics 8.0.153 YOLO Tasks Cleanup (#4314)

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Glenn Jocher
2023-08-12 02:30:57 +02:00
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docs/datasets/index.md
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@ -52,6 +52,12 @@ Image classification is a computer vision task that involves categorizing an ima
- [Imagewoof](classify/imagewoof.md): A more challenging subset of ImageNet containing 10 dog breed categories for image classification tasks.
- [MNIST](classify/mnist.md): A dataset of 70,000 grayscale images of handwritten digits for image classification tasks.
## [Oriented Bounding Boxes (OBB)](obb/index.md)
Oriented Bounding Boxes (OBB) is a method in computer vision for detecting angled objects in images using rotated bounding boxes, often applied to aerial and satellite imagery.
- [DOTAv2](obb/dota-v2.md): A popular OBB aerial imagery dataset with 1.7 million instances and 11,268 images.
## [Multi-Object Tracking](track/index.md)
Multi-object tracking is a computer vision technique that involves detecting and tracking multiple objects over time in a video sequence.

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docs/datasets/obb/index.md
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---
comments: true
description: Dive deep into various oriented bounding box (OBB) dataset formats compatible with the Ultralytics YOLO model. Grasp the nuances of using and converting datasets to this format.
description: Dive deep into various oriented bounding box (OBB) dataset formats compatible with Ultralytics YOLO models. Grasp the nuances of using and converting datasets to this format.
keywords: Ultralytics, YOLO, oriented bounding boxes, OBB, dataset formats, label formats, DOTA v2, data conversion
---
# Oriented Bounding Box Datasets Overview
# Oriented Bounding Box (OBB) Datasets Overview
Training a precise object detection model with oriented bounding boxes (OBB) requires a thorough dataset. This guide elucidates the various OBB dataset formats compatible with the Ultralytics YOLO model, offering insights into their structure, application, and methods for format conversions.
Training a precise object detection model with oriented bounding boxes (OBB) requires a thorough dataset. This guide explains the various OBB dataset formats compatible with Ultralytics YOLO models, offering insights into their structure, application, and methods for format conversions.
## Supported OBB Dataset Formats

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docs/modes/train.md
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@ -160,7 +160,7 @@ Training settings for YOLO models refer to the various hyperparameters and confi
| `single_cls` | `False` | train multi-class data as single-class |
| `rect` | `False` | rectangular training with each batch collated for minimum padding |
| `cos_lr` | `False` | use cosine learning rate scheduler |
| `close_mosaic` | `0` | (int) disable mosaic augmentation for final epochs |
| `close_mosaic` | `10` | (int) disable mosaic augmentation for final epochs (0 to disable) |
| `resume` | `False` | resume training from last checkpoint |
| `amp` | `True` | Automatic Mixed Precision (AMP) training, choices=[True, False] |
| `fraction` | `1.0` | dataset fraction to train on (default is 1.0, all images in train set) |

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docs/usage/cfg.md
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@ -102,7 +102,7 @@ The training settings for YOLO models encompass various hyperparameters and conf
| `single_cls` | `False` | train multi-class data as single-class |
| `rect` | `False` | rectangular training with each batch collated for minimum padding |
| `cos_lr` | `False` | use cosine learning rate scheduler |
| `close_mosaic` | `0` | (int) disable mosaic augmentation for final epochs |
| `close_mosaic` | `10` | (int) disable mosaic augmentation for final epochs (0 to disable) |
| `resume` | `False` | resume training from last checkpoint |
| `amp` | `True` | Automatic Mixed Precision (AMP) training, choices=[True, False] |
| `fraction` | `1.0` | dataset fraction to train on (default is 1.0, all images in train set) |