@ -16,28 +16,30 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
## Training Integrations
- [Comet ML](comet.md): Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments.
- [ClearML](clearml.md): Automate your Ultralytics ML workflows, monitor experiments, and foster team collaboration.
- [DVC](dvc.md): Implement version control for your Ultralytics machine learning projects, synchronizing data, code, and models effectively.
- [Comet ML](comet.md): Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments.
- [Ultralytics HUB](https://hub.ultralytics.com): Access and contribute to a community of pre-trained Ultralytics models.
- [DVC](dvc.md): Implement version control for your Ultralytics machine learning projects, synchronizing data, code, and models effectively.
- [MLFlow](mlflow.md): Streamline the entire ML lifecycle of Ultralytics models, from experimentation and reproducibility to deployment.
- [Ultralytics HUB](https://hub.ultralytics.com): Access and contribute to a community of pre-trained Ultralytics models.
- [Neptune](https://neptune.ai/): Maintain a comprehensive log of your ML experiments with Ultralytics in this metadata store designed for MLOps.
- [Ray Tune](ray-tune.md): Optimize the hyperparameters of your Ultralytics models at any scale.
- [TensorBoard](https://tensorboard.dev/): Visualize your Ultralytics ML workflows, monitor model metrics, and foster team collaboration.
- [Weights & Biases (W&B)](https://wandb.ai/site): Monitor experiments, visualize metrics, and foster reproducibility and collaboration on Ultralytics projects.
- [Weights & Biases (W&B)](weights-biases.md): Monitor experiments, visualize metrics, and foster reproducibility and collaboration on Ultralytics projects.
## Deployment Integrations
- [Neural Magic](https://neuralmagic.com/): Leverage Quantization Aware Training (QAT) and pruning techniques to optimize Ultralytics models for superior performance and leaner size.
- [OpenVino](openvino.md): OpenVINO is Intel's toolkit for optimizing and deploying computer vision models efficiently across various Intel hardware platforms.
### Export Formats
We also support a variety of model export formats for deployment in different environments. Here are the available formats:
# Enhancing YOLOv8 Experiment Tracking and Visualization with Weights & Biases
Object detection models like [Ultralytics’ YOLOv8](https://ultralytics.com) have become integral to many computer vision 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 models like [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) have become integral to many computer vision 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.
This guide showcases Ultralytics YOLOv8 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.
@ -46,7 +46,6 @@ Start by initializing the Weights & Biases environment in your workspace. You ca
!!! Tip "Initial SDK Setup"
=== "CLI"
```bash
# Initialize your Weights & Biases environment
import wandb
@ -62,7 +61,6 @@ Before diving into the usage instructions for YOLOv8 model training with Weights
!!! Example "Usage: Training YOLOv8 with Weights & Biases"
=== "Python"
```python
from ultralytics import YOLO
from wandb.integration.ultralytics import add_wandb_callback
@ -96,19 +94,19 @@ Before diving into the usage instructions for YOLOv8 model training with Weights
Let’s understand the steps showcased in the usage code snippet above.
**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.
- **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.
**Step 2: Define the YOLOv8 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.
- **Step 2: Define the YOLOv8 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.
**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.
- **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.
**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, offering a comprehensive view of the model's learning progress.
- **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, offering a comprehensive view of the model's learning progress.
**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 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 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.
- **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.
### Understanding the Output
@ -124,11 +122,11 @@ Upon running the usage code snippet above, you can expect the following key outp
After running the usage code snippet, you can access the Weights & Biases (W&B) dashboard through the provided link in the output. This dashboard offers a comprehensive view of your model's training process with YOLOv8.
#### Key Features of the Weights & Biases Dashboard
## Key Features of the Weights & Biases Dashboard
- **Real-Time Metrics Tracking**: Observe metrics like loss, accuracy, and validation scores as they evolve during the training, offering immediate insights for model tuning.
-<blockquoteclass="imgur-embed-pub"lang="en"data-id="a/TB76U9O"><ahref="//imgur.com/D6NVnmN">Take a look at how the experiments are tracked using Weights & Biases.</a></blockquote><scriptasyncsrc="//s.imgur.com/min/embed.js"charset="utf-8"></script>
<divstyle="text-align:center;"><blockquoteclass="imgur-embed-pub"lang="en"data-id="a/TB76U9O"><ahref="//imgur.com/D6NVnmN">Take a look at how the experiments are tracked using Weights & Biases.</a></blockquote></div><scriptasyncsrc="//s.imgur.com/min/embed.js"charset="utf-8"></script>
- **Hyperparameter Optimization**: Weights & Biases aids in fine-tuning critical parameters such as learning rate, batch size, and more, enhancing the performance of YOLOv8.
@ -136,7 +134,7 @@ After running the usage code snippet, you can access the Weights & Biases (W&B)
- **Visualization of Training Progress**: Graphical representations of key metrics provide an intuitive understanding of the model's performance across epochs.
-<blockquoteclass="imgur-embed-pub"lang="en"data-id="a/kU5h7W4"data-context="false"><ahref="//imgur.com/a/kU5h7W4">Take a look at how Weights & Biases helps you visualize validation results.</a></blockquote><scriptasyncsrc="//s.imgur.com/min/embed.js"charset="utf-8"></script>
<divstyle="text-align:center;"><blockquoteclass="imgur-embed-pub"lang="en"data-id="a/kU5h7W4"data-context="false"><ahref="//imgur.com/a/kU5h7W4">Take a look at how Weights & Biases helps you visualize validation results.</a></blockquote></div><scriptasyncsrc="//s.imgur.com/min/embed.js"charset="utf-8"></script>
- **Resource Monitoring**: Keep track of CPU, GPU, and memory usage to optimize the efficiency of the training process.
@ -144,7 +142,7 @@ After running the usage code snippet, you can access the Weights & Biases (W&B)
- **Viewing Inference Results with Image Overlay**: Visualize the prediction results on images using interactive overlays in Weights & Biases, providing a clear and detailed view of model performance on real-world data. For more detailed information on Weights & Biases’ image overlay capabilities, check out this [link](https://docs.wandb.ai/guides/track/log/media#image-overlays).
-<blockquoteclass="imgur-embed-pub"lang="en"data-id="a/UTSiufs"data-context="false"><ahref="//imgur.com/a/UTSiufs">Take a look at how Weights & Biases’ image overlays helps visualize model inferences.</a></blockquote><scriptasyncsrc="//s.imgur.com/min/embed.js"charset="utf-8"></script>
<divstyle="text-align:center;"><blockquoteclass="imgur-embed-pub"lang="en"data-id="a/UTSiufs"data-context="false"><ahref="//imgur.com/a/UTSiufs">Take a look at how Weights & Biases’ image overlays helps visualize model inferences.</a></blockquote></div><scriptasyncsrc="//s.imgur.com/min/embed.js"charset="utf-8"></script>
By using these features, you can effectively track, analyze, and optimize your YOLOv8 model's training, ensuring the best possible performance and efficiency.
Glenn Jocher
11 months ago
committed by
GitHub