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ultralytics 8.0.141 create new SettingsManager (#3790)

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Glenn Jocher
2023-07-23 16:03:34 +02:00
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6
docs/usage/callbacks.md
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@ -20,10 +20,10 @@ In this example, we want to return the original frame with each result object. H
def on_predict_batch_end(predictor):
# Retrieve the batch data
_, im0s, _, _ = predictor.batch
# Ensure that im0s is a list
im0s = im0s if isinstance(im0s, list) else [im0s]
# Combine the prediction results with the corresponding frames
predictor.results = zip(predictor.results, im0s)
@ -85,4 +85,4 @@ Here are all supported callbacks. See callbacks [source code](https://github.com
| Callback | Description |
|-------------------|------------------------------------------|
| `on_export_start` | Triggered when the export process starts |
| `on_export_end` | Triggered when the export process ends |
| `on_export_end` | Triggered when the export process ends |

26
docs/usage/cfg.md
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@ -13,19 +13,19 @@ YOLOv8 'yolo' CLI commands use the following syntax:
!!! example ""
=== "CLI"
```bash
yolo TASK MODE ARGS
```
=== "Python"
```python
from ultralytics import YOLO
# Load a YOLOv8 model from a pre-trained weights file
model = YOLO('yolov8n.pt')
# Run MODE mode using the custom arguments ARGS (guess TASK)
model.MODE(ARGS)
```
@ -45,9 +45,9 @@ Where:
YOLO models can be used for a variety of tasks, including detection, segmentation, classification and pose. These tasks
differ in the type of output they produce and the specific problem they are designed to solve.
**Detect**: For identifying and localizing objects or regions of interest in an image or video.
**Segment**: For dividing an image or video into regions or pixels that correspond to different objects or classes.
**Classify**: For predicting the class label of an input image.
**Detect**: For identifying and localizing objects or regions of interest in an image or video.
**Segment**: For dividing an image or video into regions or pixels that correspond to different objects or classes.
**Classify**: For predicting the class label of an input image.
**Pose**: For identifying objects and estimating their keypoints in an image or video.
| Key | Value | Description |
@ -61,11 +61,11 @@ differ in the type of output they produce and the specific problem they are desi
YOLO models can be used in different modes depending on the specific problem you are trying to solve. These modes
include:
**Train**: For training a YOLOv8 model on a custom dataset.
**Val**: For validating a YOLOv8 model after it has been trained.
**Predict**: For making predictions using a trained YOLOv8 model on new images or videos.
**Export**: For exporting a YOLOv8 model to a format that can be used for deployment.
**Track**: For tracking objects in real-time using a YOLOv8 model.
**Train**: For training a YOLOv8 model on a custom dataset.
**Val**: For validating a YOLOv8 model after it has been trained.
**Predict**: For making predictions using a trained YOLOv8 model on new images or videos.
**Export**: For exporting a YOLOv8 model to a format that can be used for deployment.
**Track**: For tracking objects in real-time using a YOLOv8 model.
**Benchmark**: For benchmarking YOLOv8 exports (ONNX, TensorRT, etc.) speed and accuracy.
| Key | Value | Description |
@ -251,4 +251,4 @@ it easier to debug and optimize the training process.
| `name` | `'exp'` | experiment name. `exp` gets automatically incremented if not specified, i.e, `exp`, `exp2` ... |
| `exist_ok` | `False` | whether to overwrite existing experiment |
| `plots` | `False` | save plots during train/val |
| `save` | `False` | save train checkpoints and predict results |
| `save` | `False` | save train checkpoints and predict results |

4
docs/usage/cli.md
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@ -74,7 +74,7 @@ Where:
!!! warning "Warning"
Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces ` ` between pairs. Do not use `--` argument prefixes or commas `,` beteen arguments.
Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces ` ` between pairs. Do not use `--` argument prefixes or commas `,` between arguments.
- `yolo predict model=yolov8n.pt imgsz=640 conf=0.25`   ✅
- `yolo predict model yolov8n.pt imgsz 640 conf 0.25`   ❌
@ -88,7 +88,7 @@ the [Configuration](cfg.md) page.
!!! example "Example"
=== "Train"
Start training YOLOv8n on COCO128 for 100 epochs at image-size 640.
```bash
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640

2
docs/usage/engine.md
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@ -84,4 +84,4 @@ To know more about Callback triggering events and entry point, checkout our [Cal
## Other engine components
There are other components that can be customized similarly like `Validators` and `Predictors`
See Reference section for more information on these.
See Reference section for more information on these.

6
docs/usage/hyperparameter_tuning.md
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@ -29,7 +29,7 @@ To install the required packages, run:
!!! tip "Installation"
```bash
# Install and update Ultralytics and Ray Tune pacakges
# Install and update Ultralytics and Ray Tune packages
pip install -U ultralytics 'ray[tune]'
# Optionally install W&B for logging
@ -99,7 +99,7 @@ In this example, we demonstrate how to use a custom search space for hyperparame
```python
from ultralytics import YOLO
# Define a YOLO model
# Define a YOLO model
model = YOLO("yolov8n.pt")
# Run Ray Tune on the model
@ -166,4 +166,4 @@ plt.show()
In this documentation, we covered common workflows to analyze the results of experiments run with Ray Tune using Ultralytics. The key steps include loading the experiment results from a directory, performing basic experiment-level and trial-level analysis and plotting metrics.
Explore further by looking into Ray Tune’s [Analyze Results](https://docs.ray.io/en/latest/tune/examples/tune_analyze_results.html) docs page to get the most out of your hyperparameter tuning experiments.
Explore further by looking into Ray Tune’s [Analyze Results](https://docs.ray.io/en/latest/tune/examples/tune_analyze_results.html) docs page to get the most out of your hyperparameter tuning experiments.

30
docs/usage/python.md
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@ -19,22 +19,22 @@ format with just a few lines of code.
```python
from ultralytics import YOLO
# Create a new YOLO model from scratch
model = YOLO('yolov8n.yaml')
# Load a pretrained YOLO model (recommended for training)
model = YOLO('yolov8n.pt')
# Train the model using the 'coco128.yaml' dataset for 3 epochs
results = model.train(data='coco128.yaml', epochs=3)
# Evaluate the model's performance on the validation set
results = model.val()
# Perform object detection on an image using the model
results = model('https://ultralytics.com/images/bus.jpg')
# Export the model to ONNX format
success = model.export(format='onnx')
```
@ -135,7 +135,7 @@ predicts the classes and locations of objects in the input images or videos.
=== "Results usage"
```python
# results would be a list of Results object including all the predictions by default
# but be careful as it could occupy a lot memory when there're many images,
# but be careful as it could occupy a lot memory when there're many images,
# especially the task is segmentation.
# 1. return as a list
results = model.predict(source="folder")
@ -161,7 +161,7 @@ predicts the classes and locations of objects in the input images or videos.
# Classification
result.probs # cls prob, (num_class, )
# Each result is composed of torch.Tensor by default,
# Each result is composed of torch.Tensor by default,
# in which you can easily use following functionality:
result = result.cuda()
result = result.cpu()
@ -210,18 +210,18 @@ for applications such as surveillance systems or self-driving cars.
!!! example "Track"
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # load an official detection model
model = YOLO('yolov8n-seg.pt') # load an official segmentation model
model = YOLO('path/to/best.pt') # load a custom model
# Track with the model
results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True)
results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True, tracker="bytetrack.yaml")
results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True)
results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True, tracker="bytetrack.yaml")
```
[Track Examples](../modes/track.md){ .md-button .md-button--primary}
@ -237,11 +237,11 @@ their specific use case based on their requirements for speed and accuracy.
!!! example "Benchmark"
=== "Python"
Benchmark an official YOLOv8n model across all export formats.
```python
from ultralytics.utils.benchmarks import benchmark
# Benchmark
benchmark(model='yolov8n.pt', data='coco8.yaml', imgsz=640, half=False, device=0)
```