HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Update W&B docs. (#16708)

Browse source 
Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Skillnoob_ <makei05@outlook.de>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Mohammed Yasin 2024-10-06 00:32:38 +08:00 • committed by GitHub
parent e45e3b16ad
commit e56f9140fb
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
5 changed files with 107 additions and 107 deletions
Download patch file Download diff file Expand all files Collapse all files

205
docs/en/integrations/weights-biases.md
View file

@ -6,7 +6,7 @@ keywords: YOLO11, Weights & Biases, model training, experiment tracking, Ultraly
# Enhancing YOLO11 Experiment Tracking and Visualization with Weights & Biases
[Object detection](https://www.ultralytics.com/glossary/object-detection) models like [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) have become integral to many [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications. However, training, evaluating, and deploying these complex models introduces several challenges. Tracking key training metrics, comparing model variants, analyzing model behavior, and detecting issues require substantial instrumentation and experiment management.
[Object detection](https://www.ultralytics.com/glossary/object-detection) models like [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) have become integral to many [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) applications. However, training, evaluating, and deploying these complex models introduce several challenges. Tracking key training metrics, comparing model variants, analyzing model behavior, and detecting issues require significant instrumentation and experiment management.
<p align="center">
<br>
@ -19,7 +19,7 @@ keywords: YOLO11, Weights & Biases, model training, experiment tracking, Ultraly
<strong>Watch:</strong> How to use Ultralytics YOLO11 with Weights and Biases
</p>
This guide showcases Ultralytics YOLO11 integration with Weights & Biases' for enhanced experiment tracking, model-checkpointing, and visualization of model performance. It also includes instructions for setting up the integration, training, fine-tuning, and visualizing results using Weights & Biases' interactive features.
This guide showcases Ultralytics YOLO11 integration with Weights & Biases for enhanced experiment tracking, model-checkpointing, and visualization of model performance. It also includes instructions for setting up the integration, training, fine-tuning, and visualizing results using Weights & Biases' interactive features.
## Weights & Biases
@ -42,8 +42,8 @@ To install the required packages, run:
=== "CLI"
```bash
# Install the required packages for YOLO11 and Weights & Biases
pip install --upgrade ultralytics==8.0.186 wandb
# Install the required packages for Ultralytics YOLO and Weights & Biases
pip install -U ultralytics wandb
```
For detailed instructions and best practices related to the installation process, be sure to check our [YOLO11 Installation guide](../quickstart.md). While installing the required packages for YOLO11, if you encounter any difficulties, consult our [Common Issues guide](../guides/yolo-common-issues.md) for solutions and tips.
@ -56,12 +56,20 @@ Start by initializing the Weights & Biases environment in your workspace. You ca
!!! tip "Initial SDK Setup"
=== "Python"
```python
import wandb
# Initialize your Weights & Biases environment
wandb.login(key="<API_KEY>")
```
=== "CLI"
```bash
# Initialize your Weights & Biases environment
import wandb
wandb.login()
wandb login <API_KEY>
```
Navigate to the Weights & Biases authorization page to create and retrieve your API key. Use this key to authenticate your environment with W&B.
@ -75,50 +83,39 @@ Before diving into the usage instructions for YOLO11 model training with Weights
=== "Python"
```python
import wandb
from wandb.integration.ultralytics import add_wandb_callback
from ultralytics import YOLO
# Initialize a Weights & Biases run
wandb.init(project="ultralytics", job_type="training")
# Load a YOLO model
model = YOLO("yolo11n.pt")
# Add W&B Callback for Ultralytics
add_wandb_callback(model, enable_model_checkpointing=True)
# Train and Fine-Tune the Model
model.train(project="ultralytics", data="coco8.yaml", epochs=5, imgsz=640)
# Validate the Model
model.val()
# Perform Inference and Log Results
model(["path/to/image1", "path/to/image2"])
# Finalize the W&B Run
wandb.finish()
model.train(data="coco8.yaml", epochs=5, project="ultralytics", name="yolo11n")
```
### Understanding the Code
=== "CLI"
Let's understand the steps showcased in the usage code snippet above.
```bash
# Train a YOLO11 model with Weights & Biases
yolo train data=coco8.yaml epochs=5 project=ultralytics name=yolo11n
```
- **Step 1: Initialize a Weights & Biases Run**: Start by initializing a Weights & Biases run, specifying the project name and the job type. This run will track and manage the training and validation processes of your model.
### W&B Arguments
- **Step 2: Define the YOLO11 Model and Dataset**: Specify the model variant and the dataset you wish to use. The YOLO model is then initialized with the specified model file.
| Argument | Default | Description |
| -------- | ------- | ------------------------------------------------------------------------------------------------------------------ |
| project | `None` | Specifies the name of the project logged locally and in W&B. This way you can group multiple runs together. |
| name | `None` | The name of the training run. This determines the name used to create subfolders and the name used for W&B logging |
- **Step 3: Add Weights & Biases Callback for Ultralytics**: This step is crucial as it enables the automatic logging of training metrics and validation results to Weights & Biases, providing a detailed view of the model's performance.
!!! Tip "Enable or Disable Weights & Biases"
If you want to enable or disable Weights & Biases logging, you can use the `wandb` command. By default, Weights & Biases logging is enabled.
- **Step 4: Train and Fine-Tune the Model**: Begin training the model with the specified dataset, number of epochs, and image size. The training process includes logging of metrics and predictions at the end of each [epoch](https://www.ultralytics.com/glossary/epoch), offering a comprehensive view of the model's learning progress.
```bash
# Enable Weights & Biases logging
wandb enabled
- **Step 5: Validate the Model**: After training, the model is validated. This step is crucial for assessing the model's performance on unseen data and ensuring its generalizability.
- **Step 6: Perform Inference and Log Results**: The model performs predictions on specified images. These predictions, along with visual overlays and insights, are automatically logged in a W&B Table for interactive exploration.
- **Step 7: Finalize the W&B Run**: This step marks the end of data logging and saves the final state of your model's training and validation process in the W&B dashboard.
# Disable Weights & Biases logging
wandb disabled
```
### Understanding the Output
@ -126,7 +123,7 @@ Upon running the usage code snippet above, you can expect the following key outp
- The setup of a new run with its unique ID, indicating the start of the training process.
- A concise summary of the model's structure, including the number of layers and parameters.
- Regular updates on important metrics such as box loss, cls loss, dfl loss, [precision](https://www.ultralytics.com/glossary/precision), [recall](https://www.ultralytics.com/glossary/recall), and mAP scores during each training epoch.
- Regular updates on important metrics such as box loss, cls loss, dfl loss, [precision](https://www.ultralytics.com/glossary/precision), [recall](https://www.ultralytics.com/glossary/recall), and mAP scores during each training [epoch](https://www.ultralytics.com/glossary/epoch).
- At the end of training, detailed metrics including the model's inference speed, and overall [accuracy](https://www.ultralytics.com/glossary/accuracy) metrics are displayed.
- Links to the Weights & Biases dashboard for in-depth analysis and visualization of the training process, along with information on local log file locations.
@ -138,7 +135,7 @@ After running the usage code snippet, you can access the Weights & Biases (W&B)
- **Real-Time Metrics Tracking**: Observe metrics like loss, accuracy, and validation scores as they evolve during the training, offering immediate insights for model tuning. [See how experiments are tracked using Weights & Biases](https://imgur.com/D6NVnmN).
- **Hyperparameter Optimization**: Weights & Biases aids in fine-tuning critical parameters such as [learning rate](https://www.ultralytics.com/glossary/learning-rate), batch size, and more, enhancing the performance of YOLO11.
- **Hyperparameter Optimization**: Weights & Biases aids in fine-tuning critical parameters such as [learning rate](https://www.ultralytics.com/glossary/learning-rate), [batch size](https://www.ultralytics.com/glossary/batch-size), and more, enhancing the performance of YOLO11.
- **Comparative Analysis**: The platform allows side-by-side comparisons of different training runs, essential for assessing the impact of various model configurations.
@ -154,7 +151,7 @@ By using these features, you can effectively track, analyze, and optimize your Y
## Summary
This guide helped you explore Ultralytics' YOLO11 integration with Weights & Biases. It illustrates the ability of this integration to efficiently track and visualize model training and prediction results.
This guide helped you explore the Ultralytics YOLO integration with Weights & Biases. It illustrates the ability of this integration to efficiently track and visualize model training and prediction results.
For further details on usage, visit [Weights & Biases' official documentation](https://docs.wandb.ai/guides/integrations/ultralytics/).
@ -162,83 +159,83 @@ Also, be sure to check out the [Ultralytics integration guide page](../integrati
## FAQ
### How do I install the required packages for YOLO11 and Weights & Biases?
### How do I integrate Weights & Biases with Ultralytics YOLO11?
To install the required packages for YOLO11 and Weights & Biases, open your command line interface and run:
To integrate Weights & Biases with Ultralytics YOLO11:
1. Install the required packages:
```bash
pip install --upgrade ultralytics==8.0.186 wandb
pip install -U ultralytics wandb
```
For further guidance on installation steps, refer to our [YOLO11 Installation guide](../quickstart.md). If you encounter issues, consult the [Common Issues guide](../guides/yolo-common-issues.md) for troubleshooting tips.
### What are the benefits of integrating Ultralytics YOLO11 with Weights & Biases?
Integrating Ultralytics YOLO11 with Weights & Biases offers several benefits including:
- **Real-Time Metrics Tracking:** Observe metric changes during training for immediate insights.
- **Hyperparameter Optimization:** Improve model performance by fine-tuning learning rate, [batch size](https://www.ultralytics.com/glossary/batch-size), etc.
- **Comparative Analysis:** Side-by-side comparison of different training runs.
- **Resource Monitoring:** Keep track of CPU, GPU, and memory usage.
- **Model Artifacts Management:** Easy access and sharing of model checkpoints.
Explore these features in detail in the Weights & Biases Dashboard section above.
### How can I configure Weights & Biases for YOLO11 training?
To configure Weights & Biases for YOLO11 training, follow these steps:
1. Run the command to initialize Weights & Biases:
```bash
import wandb
wandb.login()
```
2. Retrieve your API key from the Weights & Biases website.
3. Use the API key to authenticate your development environment.
Detailed setup instructions can be found in the Configuring Weights & Biases section above.
### How do I train a YOLO11 model using Weights & Biases?
For training a YOLO11 model using Weights & Biases, use the following steps in a Python script:
2. Log in to your Weights & Biases account:
```python
import wandb
from wandb.integration.ultralytics import add_wandb_callback
from ultralytics import YOLO
# Initialize a Weights & Biases run
wandb.init(project="ultralytics", job_type="training")
# Load a YOLO model
model = YOLO("yolo11n.pt")
# Add W&B Callback for Ultralytics
add_wandb_callback(model, enable_model_checkpointing=True)
# Train and Fine-Tune the Model
model.train(project="ultralytics", data="coco8.yaml", epochs=5, imgsz=640)
# Validate the Model
model.val()
# Perform Inference and Log Results
model(["path/to/image1", "path/to/image2"])
# Finalize the W&B Run
wandb.finish()
wandb.login(key="<API_KEY>")
```
This script initializes Weights & Biases, sets up the model, trains it, and logs results. For more details, visit the Usage section above.
3. Train your YOLO11 model with W&B logging enabled:
### Why should I use Ultralytics YOLO11 with Weights & Biases over other platforms?
```python
from ultralytics import YOLO
Ultralytics YOLO11 integrated with Weights & Biases offers several unique advantages:
model = YOLO("yolo11n.pt")
model.train(data="coco8.yaml", epochs=5, project="ultralytics", name="yolo11n")
```
- **High Efficiency:** Real-time tracking of training metrics and performance optimization.
- **Scalability:** Easily manage large-scale training jobs with robust resource monitoring and utilization tools.
- **Interactivity:** A user-friendly interactive UI for [data visualization](https://www.ultralytics.com/glossary/data-visualization) and model management.
- **Community and Support:** Strong integration documentation and community support with flexible customization and enhancement options.
This will automatically log metrics, hyperparameters, and model artifacts to your W&B project.
For comparisons with other platforms like Comet and ClearML, refer to [Ultralytics integrations](../integrations/index.md).
### What are the key features of Weights & Biases integration with YOLO11?
The key features include:
- Real-time metrics tracking during training
- Hyperparameter optimization tools
- Comparative analysis of different training runs
- Visualization of training progress through graphs
- Resource monitoring (CPU, GPU, memory usage)
- Model artifacts management and sharing
- Viewing inference results with image overlays
These features help in tracking experiments, optimizing models, and collaborating more effectively on YOLO11 projects.
### How can I view the Weights & Biases dashboard for my YOLO11 training?
After running your training script with W&B integration:
1. A link to your W&B dashboard will be provided in the console output.
2. Click on the link or go to [wandb.ai](https://wandb.ai) and log in to your account.
3. Navigate to your project to view detailed metrics, visualizations, and model performance data.
The dashboard offers insights into your model's training process, allowing you to analyze and improve your YOLO11 models effectively.
### Can I disable Weights & Biases logging for YOLO11 training?
Yes, you can disable W&B logging using the following command:
```bash
wandb disabled
```
To re-enable logging, use:
```bash
wandb enabled
```
This allows you to control when you want to use W&B logging without modifying your training scripts.
### How does Weights & Biases help in optimizing YOLO11 models?
Weights & Biases helps optimize YOLO11 models by:
1. Providing detailed visualizations of training metrics
2. Enabling easy comparison between different model versions
3. Offering tools for [hyperparameter tuning](https://www.ultralytics.com/glossary/hyperparameter-tuning)
4. Allowing for collaborative analysis of model performance
5. Facilitating easy sharing of model artifacts and results
These features help researchers and developers iterate faster and make data-driven decisions to improve their YOLO11 models.