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ultralytics 8.0.148 fix SettingsManager empty YAML bug (#4180)

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Glenn Jocher
2023-08-05 16:13:29 +02:00
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commit 038558cfab
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6
docs/guides/kfold-cross-validation.md
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@ -4,12 +4,16 @@ description: An in-depth guide demonstrating the implementation of K-Fold Cross
keywords: K-Fold cross validation, Ultralytics, YOLO detection format, Python, sklearn, object detection
---
# K-Fold Cross Validation in the Ultralytics Ecosystem
# K-Fold Cross Validation with Ultralytics
## Introduction
This comprehensive guide illustrates the implementation of K-Fold Cross Validation for object detection datasets within the Ultralytics ecosystem. We'll leverage the YOLO detection format and key Python libraries such as sklearn, pandas, and PyYaml to guide you through the necessary setup, the process of generating feature vectors, and the execution of a K-Fold dataset split.
<p align="center">
<img width="800" src="https://user-images.githubusercontent.com/26833433/258589390-8d815058-ece8-48b9-a94e-0e1ab53ea0f6.png" alt="K-Fold Cross Validation Overview">
</p>
Whether your project involves the Fruit Detection dataset or a custom data source, this tutorial aims to help you comprehend and apply K-Fold Cross Validation to bolster the reliability and robustness of your machine learning models. While we're applying `k=5` folds for this tutorial, keep in mind that the optimal number of folds can vary depending on your dataset and the specifics of your project.
Without further ado, let's dive in!

2
docs/integrations/openvino.md
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@ -4,7 +4,7 @@ description: Discover the power of deploying your Ultralytics YOLOv8 model using
keywords: ultralytics docs, YOLOv8, export YOLOv8, YOLOv8 model deployment, exporting YOLOv8, OpenVINO, OpenVINO format
---
<img width="1024" src="https://user-images.githubusercontent.com/26833433/252345644-0cf84257-4b34-404c-b7ce-eb73dfbcaff1.png">
<img width="1024" src="https://user-images.githubusercontent.com/26833433/252345644-0cf84257-4b34-404c-b7ce-eb73dfbcaff1.png" alt="OpenVINO Ecosystem">
**Export mode** is used for exporting a YOLOv8 model to a format that can be used for deployment. In this guide, we specifically cover exporting to OpenVINO, which can provide up to 3x [CPU](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_CPU.html) speedup as well as accelerating on other Intel hardware ([iGPU](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_GPU.html), [dGPU](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_GPU.html), [VPU](https://docs.openvino.ai/2022.3/openvino_docs_OV_UG_supported_plugins_VPU.html), etc.).

69
docs/integrations/ray-tune.md
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@ -14,7 +14,9 @@ Hyperparameter tuning is vital in achieving peak model performance by discoverin
### Ray Tune
![Ray Tune Overview](https://docs.ray.io/en/latest/_images/tune_overview.png)
<p align="center">
<img width="640" src="https://docs.ray.io/en/latest/_images/tune_overview.png" alt="Ray Tune Overview">
</p>
[Ray Tune](https://docs.ray.io/en/latest/tune/index.html) is a hyperparameter tuning library designed for efficiency and flexibility. It supports various search strategies, parallelism, and early stopping strategies, and seamlessly integrates with popular machine learning frameworks, including Ultralytics YOLOv8.
@ -43,7 +45,10 @@ To install the required packages, run:
```python
from ultralytics import YOLO
# Load a YOLOv8n model
model = YOLO("yolov8n.pt")
# Start tuning hyperparameters for YOLOv8n training on the COCO128 dataset
result_grid = model.tune(data="coco128.yaml")
```
@ -51,14 +56,14 @@ To install the required packages, run:
The `tune()` method in YOLOv8 provides an easy-to-use interface for hyperparameter tuning with Ray Tune. It accepts several arguments that allow you to customize the tuning process. Below is a detailed explanation of each parameter:
| Parameter | Type | Description | Default Value |
|-----------------|----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------|
| `data` | str | The dataset configuration file (in YAML format) to run the tuner on. This file should specify the training and validation data paths, as well as other dataset-specific settings. | |
| `space` | dict, optional | A dictionary defining the hyperparameter search space for Ray Tune. Each key corresponds to a hyperparameter name, and the value specifies the range of values to explore during tuning. If not provided, YOLOv8 uses a default search space with various hyperparameters. | |
| `grace_period` | int, optional | The grace period in epochs for the [ASHA scheduler](https://docs.ray.io/en/latest/tune/api/schedulers.html) in Ray Tune. The scheduler will not terminate any trial before this number of epochs, allowing the model to have some minimum training before making a decision on early stopping. | 10 |
| `gpu_per_trial` | int, optional | The number of GPUs to allocate per trial during tuning. This helps manage GPU usage, particularly in multi-GPU environments. If not provided, the tuner will use all available GPUs. | None |
| `max_samples` | int, optional | The maximum number of trials to run during tuning. This parameter helps control the total number of hyperparameter combinations tested, ensuring the tuning process does not run indefinitely. | 10 |
| `**train_args` | dict, optional | Additional arguments to pass to the `train()` method during tuning. These arguments can include settings like the number of training epochs, batch size, and other training-specific configurations. | {} |
| Parameter | Type | Description | Default Value |
|-----------------|------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------|
| `data` | `str` | The dataset configuration file (in YAML format) to run the tuner on. This file should specify the training and validation data paths, as well as other dataset-specific settings. | |
| `space` | `dict, optional` | A dictionary defining the hyperparameter search space for Ray Tune. Each key corresponds to a hyperparameter name, and the value specifies the range of values to explore during tuning. If not provided, YOLOv8 uses a default search space with various hyperparameters. | |
| `grace_period` | `int, optional` | The grace period in epochs for the [ASHA scheduler](https://docs.ray.io/en/latest/tune/api/schedulers.html) in Ray Tune. The scheduler will not terminate any trial before this number of epochs, allowing the model to have some minimum training before making a decision on early stopping. | 10 |
| `gpu_per_trial` | `int, optional` | The number of GPUs to allocate per trial during tuning. This helps manage GPU usage, particularly in multi-GPU environments. If not provided, the tuner will use all available GPUs. | None |
| `max_samples` | `int, optional` | The maximum number of trials to run during tuning. This parameter helps control the total number of hyperparameter combinations tested, ensuring the tuning process does not run indefinitely. | 10 |
| `**train_args` | `dict, optional` | Additional arguments to pass to the `train()` method during tuning. These arguments can include settings like the number of training epochs, batch size, and other training-specific configurations. | {} |
By customizing these parameters, you can fine-tune the hyperparameter optimization process to suit your specific needs and available computational resources.
@ -66,29 +71,29 @@ By customizing these parameters, you can fine-tune the hyperparameter optimizati
The following table lists the default search space parameters for hyperparameter tuning in YOLOv8 with Ray Tune. Each parameter has a specific value range defined by `tune.uniform()`.
| Parameter | Value Range | Description |
|-----------------|----------------------------|------------------------------------------|
| lr0 | `tune.uniform(1e-5, 1e-1)` | Initial learning rate |
| lrf | `tune.uniform(0.01, 1.0)` | Final learning rate factor |
| momentum | `tune.uniform(0.6, 0.98)` | Momentum |
| weight_decay | `tune.uniform(0.0, 0.001)` | Weight decay |
| warmup_epochs | `tune.uniform(0.0, 5.0)` | Warmup epochs |
| warmup_momentum | `tune.uniform(0.0, 0.95)` | Warmup momentum |
| box | `tune.uniform(0.02, 0.2)` | Box loss weight |
| cls | `tune.uniform(0.2, 4.0)` | Class loss weight |
| hsv_h | `tune.uniform(0.0, 0.1)` | Hue augmentation range |
| hsv_s | `tune.uniform(0.0, 0.9)` | Saturation augmentation range |
| hsv_v | `tune.uniform(0.0, 0.9)` | Value (brightness) augmentation range |
| degrees | `tune.uniform(0.0, 45.0)` | Rotation augmentation range (degrees) |
| translate | `tune.uniform(0.0, 0.9)` | Translation augmentation range |
| scale | `tune.uniform(0.0, 0.9)` | Scaling augmentation range |
| shear | `tune.uniform(0.0, 10.0)` | Shear augmentation range (degrees) |
| perspective | `tune.uniform(0.0, 0.001)` | Perspective augmentation range |
| flipud | `tune.uniform(0.0, 1.0)` | Vertical flip augmentation probability |
| fliplr | `tune.uniform(0.0, 1.0)` | Horizontal flip augmentation probability |
| mosaic | `tune.uniform(0.0, 1.0)` | Mosaic augmentation probability |
| mixup | `tune.uniform(0.0, 1.0)` | Mixup augmentation probability |
| copy_paste | `tune.uniform(0.0, 1.0)` | Copy-paste augmentation probability |
| Parameter | Value Range | Description |
|-------------------|----------------------------|------------------------------------------|
| `lr0` | `tune.uniform(1e-5, 1e-1)` | Initial learning rate |
| `lrf` | `tune.uniform(0.01, 1.0)` | Final learning rate factor |
| `momentum` | `tune.uniform(0.6, 0.98)` | Momentum |
| `weight_decay` | `tune.uniform(0.0, 0.001)` | Weight decay |
| `warmup_epochs` | `tune.uniform(0.0, 5.0)` | Warmup epochs |
| `warmup_momentum` | `tune.uniform(0.0, 0.95)` | Warmup momentum |
| `box` | `tune.uniform(0.02, 0.2)` | Box loss weight |
| `cls` | `tune.uniform(0.2, 4.0)` | Class loss weight |
| `hsv_h` | `tune.uniform(0.0, 0.1)` | Hue augmentation range |
| `hsv_s` | `tune.uniform(0.0, 0.9)` | Saturation augmentation range |
| `hsv_v` | `tune.uniform(0.0, 0.9)` | Value (brightness) augmentation range |
| `degrees` | `tune.uniform(0.0, 45.0)` | Rotation augmentation range (degrees) |
| `translate` | `tune.uniform(0.0, 0.9)` | Translation augmentation range |
| `scale` | `tune.uniform(0.0, 0.9)` | Scaling augmentation range |
| `shear` | `tune.uniform(0.0, 10.0)` | Shear augmentation range (degrees) |
| `perspective` | `tune.uniform(0.0, 0.001)` | Perspective augmentation range |
| `flipud` | `tune.uniform(0.0, 1.0)` | Vertical flip augmentation probability |
| `fliplr` | `tune.uniform(0.0, 1.0)` | Horizontal flip augmentation probability |
| `mosaic` | `tune.uniform(0.0, 1.0)` | Mosaic augmentation probability |
| `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

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docs/modes/predict.md
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@ -571,7 +571,7 @@ You can use the `plot()` method of a `Result` objects to visualize predictions.
# Show the results
for r in results:
im_array = r.plot() # plot a BGR numpy array of predictions
im = Image.fromarray(im[..., ::-1]) # RGB PIL image
im = Image.fromarray(im_array[..., ::-1]) # RGB PIL image
im.show() # show image
im.save('results.jpg') # save image
```