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ultralytics 8.0.97 confusion matrix, windows, docs updates (#2511)

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Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Dowon <ks2515@naver.com>
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Glenn Jocher
2023-05-09 21:20:34 +02:00
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6
docs/usage/callbacks.md
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---
comments: true
description: Learn how to leverage callbacks in Ultralytics YOLO framework to perform custom tasks in trainer, validator, predictor and exporter modes.
---
## Callbacks
@ -40,7 +41,6 @@ for (result, frame) in model.track/predict():
Here are all supported callbacks. See callbacks [source code](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/utils/callbacks/base.py) for additional details.
### Trainer Callbacks
| Callback | Description |
@ -60,7 +60,6 @@ Here are all supported callbacks. See callbacks [source code](https://github.com
| `on_params_update` | Triggered when model parameters are updated |
| `teardown` | Triggered when the training process is being cleaned up |
### Validator Callbacks
| Callback | Description |
@ -70,7 +69,6 @@ Here are all supported callbacks. See callbacks [source code](https://github.com
| `on_val_batch_end` | Triggered at the end of each validation batch |
| `on_val_end` | Triggered when the validation ends |
### Predictor Callbacks
| Callback | Description |
@ -86,4 +84,4 @@ 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 |

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docs/usage/cfg.md
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---
comments: true
description: 'Learn about YOLO settings and modes for different tasks like detection, segmentation etc. Train and predict with custom argparse commands.'
---
YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings
@ -247,4 +248,4 @@ 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 |

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docs/usage/cli.md
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---
comments: true
description: Learn how to use YOLOv8 from the Command Line Interface (CLI) through simple, single-line commands with `yolo` without Python code.
---
# Command Line Interface Usage
@ -222,4 +223,4 @@ like `imgsz=320` in this example:
```bash
yolo copy-cfg
yolo cfg=default_copy.yaml imgsz=320
```
```

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docs/usage/engine.md
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---
comments: true
description: Learn how to train and customize your models fast with the Ultralytics YOLO 'DetectionTrainer' and 'CustomTrainer'. Read more here!
---
Both the Ultralytics YOLO command-line and python interfaces are simply a high-level abstraction on the base engine
@ -83,5 +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.

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docs/usage/hyperparameter_tuning.md
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---
comments: true
description: Discover how to integrate hyperparameter tuning with Ray Tune and Ultralytics YOLOv8. Speed up the tuning process and optimize your model's performance.
---
# Hyperparameter Tuning with Ray Tune and YOLOv8
@ -10,7 +11,7 @@ Hyperparameter tuning (or hyperparameter optimization) is the process of determi
[Ultralytics](https://ultralytics.com) YOLOv8 integrates hyperparameter tuning with Ray Tune, allowing you to easily optimize your YOLOv8 model's hyperparameters. By using Ray Tune, you can leverage advanced search algorithms, parallelism, and early stopping to speed up the tuning process and achieve better model performance.
### Ray Tune
### Ray Tune
<div align="center">
<a href="https://docs.ray.io/en/latest/tune/index.html" target="_blank">
@ -88,7 +89,6 @@ The following table lists the default search space parameters for hyperparameter
| mixup | `tune.uniform(0.0, 1.0)` | Mixup augmentation probability |
| copy_paste | `tune.uniform(0.0, 1.0)` | Copy-paste augmentation probability |
## Custom Search Space Example
In this example, we demonstrate how to use a custom search space for hyperparameter tuning with Ray Tune and YOLOv8. By providing a custom search space, you can focus the tuning process on specific hyperparameters of interest.

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docs/usage/python.md
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---
comments: true
description: Integrate YOLOv8 in Python. Load, use pretrained models, train, and infer images. Export to ONNX. Track objects in videos.
---
# Python Usage
@ -278,4 +279,4 @@ You can easily customize Trainers to support custom tasks or explore R&D ideas.
Learn more about Customizing `Trainers`, `Validators` and `Predictors` to suit your project needs in the Customization
Section.
[Customization tutorials](engine.md){ .md-button .md-button--primary}
[Customization tutorials](engine.md){ .md-button .md-button--primary}