> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Ultralytics YOLO
> Use W&B with Ultralytics YOLO models for experiment tracking, model checkpointing, and computer vision visualization.
export const ColabLink = ({url}) =>
Try in Colab
;
[Ultralytics](https://github.com/ultralytics/ultralytics) is the home for cutting-edge, state-of-the-art computer vision models for tasks like image classification, object detection, image segmentation, and pose estimation. Not only it hosts [YOLOv8](https://docs.ultralytics.com/models/yolov8/), the latest iteration in the YOLO series of real-time object detection models, but other powerful computer vision models such as [SAM (Segment Anything Model)](https://docs.ultralytics.com/models/sam/#introduction-to-sam-the-segment-anything-model), [RT-DETR](https://docs.ultralytics.com/models/rtdetr/), [YOLO-NAS](https://docs.ultralytics.com/models/yolo-nas/), etc. Besides providing implementations of these models, Ultralytics also provides us with out-of-the-box workflows for training, fine-tuning, and applying these models using an easy-to-use API.
## Get started
1. Install `ultralytics` and `wandb`.
```shell theme={null}
pip install --upgrade ultralytics==8.0.238 wandb
# or
# conda install ultralytics
```
```bash theme={null}
!pip install --upgrade ultralytics==8.0.238 wandb
```
The development team has tested the integration with `ultralyticsv8.0.238` and below. To report any issues with the integration, create a [GitHub issue](https://github.com/wandb/wandb/issues/new?template=sdk-bug.yml) with the tag `yolov8`.
## Track experiments and visualize validation results
This section demonstrates a typical workflow of using an [Ultralytics](https://docs.ultralytics.com/modes/predict/) model for training, fine-tuning, and validation and performing experiment tracking, model-checkpointing, and visualization of the model's performance using [W\&B](https://wandb.ai/site).
You can also check out about the integration in this report: [Supercharging Ultralytics with W\&B](https://wandb.ai/geekyrakshit/ultralytics/reports/Supercharging-Ultralytics-with-Weights-Biases--Vmlldzo0OTMyMDI4)
To use the W\&B integration with Ultralytics, import the `wandb.integration.ultralytics.add_wandb_callback` function.
```python theme={null}
import wandb
from wandb.integration.ultralytics import add_wandb_callback
from ultralytics import YOLO
```
Initialize the `YOLO` model of your choice, and invoke the `add_wandb_callback` function on it before performing inference with the model. This ensures that when you perform training, fine-tuning, validation, or inference, it automatically saves the experiment logs and the images, overlaid with both ground-truth and the respective prediction results using the [interactive overlays for computer vision tasks](/models/track/log/media/#image-overlays-in-tables) on W\&B along with additional insights in a [`wandb.Table`](/models/tables/).
```python theme={null}
with wandb.init(project="ultralytics", job_type="train") as run:
# Initialize YOLO Model
model = YOLO("yolov8n.pt")
# Add W&B callback for Ultralytics
add_wandb_callback(model, enable_model_checkpointing=True)
# Train/fine-tune your model
# At the end of each epoch, predictions on validation batches are logged
# to a W&B table with insightful and interactive overlays for
# computer vision tasks
model.train(project="ultralytics", data="coco128.yaml", epochs=5, imgsz=640)
```
Here's how experiments tracked using W\&B for an Ultralytics training or fine-tuning workflow looks like:
## Visualize prediction results
This section demonstrates a typical workflow of using an [Ultralytics](https://docs.ultralytics.com/modes/predict/) model for inference and visualizing the results using [W\&B](https://wandb.ai/site).
You can try out the code in Google Colab: [Open in Colab](https://wandb.me/ultralytics-inference).
You can also check out about the integration in this report: [Supercharging Ultralytics with W\&B](https://wandb.ai/geekyrakshit/ultralytics/reports/Supercharging-Ultralytics-with-Weights-Biases--Vmlldzo0OTMyMDI4)
In order to use the W\&B integration with Ultralytics, we need to import the `wandb.integration.ultralytics.add_wandb_callback` function.
```python theme={null}
import wandb
from wandb.integration.ultralytics import add_wandb_callback
from ultralytics.engine.model import YOLO
```
Download a few images to test the integration on. You can use still images, videos, or camera sources. For more information on inference sources, check out the [Ultralytics docs](https://docs.ultralytics.com/modes/predict/).
```bash theme={null}
!wget https://raw.githubusercontent.com/wandb/examples/ultralytics/colabs/ultralytics/assets/img1.png
!wget https://raw.githubusercontent.com/wandb/examples/ultralytics/colabs/ultralytics/assets/img2.png
!wget https://raw.githubusercontent.com/wandb/examples/ultralytics/colabs/ultralytics/assets/img4.png
!wget https://raw.githubusercontent.com/wandb/examples/ultralytics/colabs/ultralytics/assets/img5.png
```
Initialize a W\&B [run](/models/runs/) using `wandb.init()`. Next, Initialize your desired `YOLO` model and invoke the `add_wandb_callback` function on it before you perform inference with the model. This ensures that when you perform inference, it automatically logs the images overlaid with your [interactive overlays for computer vision tasks](/models/track/log/media/#image-overlays-in-tables) along with additional insights in a [`wandb.Table`](/models/tables/).
```python theme={null}
# Initialize W&B Run
with wandb.init(project="ultralytics", job_type="inference") as run:
# Initialize YOLO Model
model = YOLO("yolov8n.pt")
# Add W&B callback for Ultralytics
add_wandb_callback(model, enable_model_checkpointing=True)
# Perform prediction which automatically logs to a W&B Table
# with interactive overlays for bounding boxes, segmentation masks
model(
[
"./assets/img1.jpeg",
"./assets/img3.png",
"./assets/img4.jpeg",
"./assets/img5.jpeg",
]
)
```
You do not need to explicitly initialize a run using `wandb.init()` in case of a training or fine-tuning workflow. However, if the code involves only prediction, you must explicitly create a run.
Here's how the interactive bbox overlay looks:
For more details, see the [W\&B image overlays guide](/models/track/log/media/#image-overlays).
## More resources
* [Supercharging Ultralytics with W\&B](https://wandb.ai/geekyrakshit/ultralytics/reports/Supercharging-Ultralytics-with-Weights-Biases--Vmlldzo0OTMyMDI4)
* [Object Detection using YOLOv8: An End-to-End Workflow](https://wandb.ai/reviewco/object-detection-bdd/reports/Object-Detection-using-YOLOv8-An-End-to-End-Workflow--Vmlldzo1NTAyMDQ1)