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ultralytics 8.0.72 faster Windows trainings and corrupt cache fix (#1912)

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Co-authored-by: andreaswimmer <53872150+andreaswimmer@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher
2023-04-10 00:21:03 +02:00
committed by GitHub
parent 48f1d269fb
commit 95f96dc5bc
18 changed files with 312 additions and 413 deletions
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2
docs/modes/train.md
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@ -75,7 +75,7 @@ task.
| `image_weights` | `False` | use weighted image selection for training |
| `rect` | `False` | rectangular training with each batch collated for minimum padding |
| `cos_lr` | `False` | use cosine learning rate scheduler |
| `close_mosaic` | `10` | disable mosaic augmentation for final 10 epochs |
| `close_mosaic` | `0` | (int) disable mosaic augmentation for final epochs |
| `resume` | `False` | resume training from last checkpoint |
| `amp` | `True` | Automatic Mixed Precision (AMP) training, choices=[True, False] |
| `lr0` | `0.01` | initial learning rate (i.e. SGD=1E-2, Adam=1E-3) |

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docs/tasks/pose.md
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@ -23,14 +23,14 @@ the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>pose<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
|------------------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | - | 49.7 | 131.8 | 1.18 | 3.3 | 9.2 |
| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | - | 59.2 | 233.2 | 1.42 | 11.6 | 30.2 |
| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | - | 63.6 | 456.3 | 2.00 | 26.4 | 81.0 |
| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | - | 67.0 | 784.5 | 2.59 | 44.4 | 168.6 |
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | - | 68.9 | 1607.1 | 3.73 | 69.4 | 263.2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | - | 71.5 | 4088.7 | 10.04 | 99.1 | 1066.4 |
| Model | size<br><sup>(pixels) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
| ---------------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | 49.7 | 79.7 | 131.8 | 1.18 | 3.3 | 9.2 |
| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | 59.2 | 85.8 | 233.2 | 1.42 | 11.6 | 30.2 |
| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | 63.6 | 88.8 | 456.3 | 2.00 | 26.4 | 81.0 |
| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | 67.0 | 89.9 | 784.5 | 2.59 | 44.4 | 168.6 |
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | 68.9 | 90.4 | 1607.1 | 3.73 | 69.4 | 263.2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | 71.5 | 91.3 | 4088.7 | 10.04 | 99.1 | 1066.4 |
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org)
dataset.

2
docs/usage/cfg.md
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@ -97,7 +97,7 @@ The training settings for YOLO models encompass various hyperparameters and conf
| `image_weights` | `False` | use weighted image selection for training |
| `rect` | `False` | rectangular training with each batch collated for minimum padding |
| `cos_lr` | `False` | use cosine learning rate scheduler |
| `close_mosaic` | `10` | disable mosaic augmentation for final 10 epochs |
| `close_mosaic` | `0` | (int) disable mosaic augmentation for final epochs |
| `resume` | `False` | resume training from last checkpoint |
| `amp` | `True` | Automatic Mixed Precision (AMP) training, choices=[True, False] |
| `lr0` | `0.01` | initial learning rate (i.e. SGD=1E-2, Adam=1E-3) |

2
docs/yolov5/train_custom_data.md
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@ -25,7 +25,7 @@ Creating a custom model to detect your objects is an iterative process of collec
YOLOv5 models must be trained on labelled data in order to learn classes of objects in that data. There are two options for creating your dataset before you start training:
<details markdown>
<summary>Use Roboflow to manage your dataset in YOLO format</summary>
<summary>Use <a href="https://roboflow.com/?ref=ultralytics">Roboflow</a> to create your dataset in YOLO format</summary>
### 1.1 Collect Images