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Omit ultralytics/utils/callbacks from coverage (#4345)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher
2023-08-14 03:25:51 +02:00
committed by GitHub
parent d47718c367
commit c940d29d4f
18 changed files with 63 additions and 36 deletions
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2
docs/models/index.md
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@ -44,7 +44,7 @@ This example provides simple inference code for YOLO, SAM and RTDETR models. For
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results model.train(data='coco8.yaml', epochs=100, imgsz=640)
results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
# Run inference with the YOLOv8n model on the 'bus.jpg' image
results = model('path/to/bus.jpg')

2
docs/models/rtdetr.md
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@ -46,7 +46,7 @@ You can use RT-DETR for object detection tasks using the `ultralytics` pip packa
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results model.train(data='coco8.yaml', epochs=100, imgsz=640)
results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
# Run inference with the RT-DETR-l model on the 'bus.jpg' image
results = model('path/to/bus.jpg')

2
docs/models/yolo-nas.md
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@ -62,7 +62,7 @@ In this example we validate YOLO-NAS-s on the COCO8 dataset.
model.info()
# Validate the model on the COCO8 example dataset
results model.val(data='coco8.yaml')
results = model.val(data='coco8.yaml')
# Run inference with the YOLO-NAS-s model on the 'bus.jpg' image
results = model('path/to/bus.jpg')

2
docs/models/yolov3.md
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@ -69,7 +69,7 @@ You can use YOLOv3 for object detection tasks using the Ultralytics repository.
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results model.train(data='coco8.yaml', epochs=100, imgsz=640)
results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
# Run inference with the YOLOv3n model on the 'bus.jpg' image
results = model('path/to/bus.jpg')

2
docs/models/yolov5.md
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@ -74,7 +74,7 @@ You can use YOLOv5u for object detection tasks using the Ultralytics repository.
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results model.train(data='coco8.yaml', epochs=100, imgsz=640)
results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
# Run inference with the YOLOv5n model on the 'bus.jpg' image
results = model('path/to/bus.jpg')

2
docs/models/yolov6.md
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@ -56,7 +56,7 @@ You can use YOLOv6 for object detection tasks using the Ultralytics pip package.
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results model.train(data='coco8.yaml', epochs=100, imgsz=640)
results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
# Run inference with the YOLOv6n model on the 'bus.jpg' image
results = model('path/to/bus.jpg')

2
docs/models/yolov8.md
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@ -101,7 +101,7 @@ You can use YOLOv8 for object detection tasks using the Ultralytics pip package.
model.info()
# Train the model on the COCO8 example dataset for 100 epochs
results model.train(data='coco8.yaml', epochs=100, imgsz=640)
results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
# Run inference with the YOLOv8n model on the 'bus.jpg' image
results = model('path/to/bus.jpg')

10
docs/quickstart.md
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@ -31,6 +31,14 @@ Ultralytics provides various installation methods including pip, conda, and Dock
conda install -c conda-forge ultralytics
```
!!! note
If you are installing in a CUDA environment best practice is to install `ultralytics`, `pytorch` and `pytorch-cuda` in the same command to allow the conda package manager to resolve any conflicts, or else to install `pytorch-cuda` last to allow it override the CPU-specific `pytorch` package if necesary.
```bash
# Install all packages together using conda
conda install -c conda-forge -c pytorch -c nvidia ultralytics pytorch torchvision pytorch-cuda=11.8
```
=== "Git clone"
Clone the `ultralytics` repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package in editable mode `-e` using pip.
```bash
@ -259,7 +267,7 @@ The table below provides an overview of the settings available for adjustment wi
| `api_key` | `''` | `str` | Ultralytics HUB [API Key](https://hub.ultralytics.com/settings?tab=api+keys) |
| `clearml` | `True` | `bool` | Whether to use ClearML logging |
| `comet` | `True` | `bool` | Whether to use [Comet ML](https://bit.ly/yolov8-readme-comet) for experiment tracking and visualization |
| `dvc` | `True` | `bool` | Whether to use [DVC for experiment tracking](https://dvc.org/doc/dvclive/ml-frameworks/yolo) and version control |
| `dvc` | `True` | `bool` | Whether to use [DVC for experiment tracking](https://dvc.org/doc/dvclive/ml-frameworks/yolo) and version control |
| `hub` | `True` | `bool` | Whether to use [Ultralytics HUB](https://hub.ultralytics.com) integration |
| `mlflow` | `True` | `bool` | Whether to use MLFlow for experiment tracking |
| `neptune` | `True` | `bool` | Whether to use Neptune for experiment tracking |

4
docs/reference/utils/callbacks/dvc.md
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@ -9,10 +9,6 @@ keywords: Ultralytics, YOLO, callbacks, logger, training, pretraining, machine l
Full source code for this file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/dvc.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/dvc.py). Help us fix any issues you see by submitting a [Pull Request](https://docs.ultralytics.com/help/contributing/) 🛠️. Thank you 🙏!
---
## ::: ultralytics.utils.callbacks.dvc._logger_disabled
<br><br>
---
## ::: ultralytics.utils.callbacks.dvc._log_images
<br><br>