Comments (2)
@yasinda-s hello,
Thank you for your question and for providing the configuration details. Yes, you can use the .val()
method to evaluate your model on a custom test dataset. To do this, you need to specify the data
argument with the path to your custom YAML file that includes the test dataset.
Here's how you can modify your YAML file to include the test dataset:
path: /opt/ml/input/data
train: train
val: val
test: test # Add this line to include the test dataset
names:
0: smoke
Then, you can use the model.val()
method and specify the data
argument to point to this updated YAML file:
from ultralytics import YOLO
# Load your trained model
model = YOLO("path/to/your/trained/model.pt")
# Validate the model on the test dataset
metrics = model.val(data="path/to/your/custom.yaml", split='test')
# Access the metrics
print(metrics.box.map) # mAP50-95
print(metrics.box.map50) # mAP50
print(metrics.box.map75) # mAP75
print(metrics.box.maps) # List of mAP50-95 for each category
By specifying split='test'
, you ensure that the validation is performed on the test dataset defined in your YAML file.
If you encounter any issues or have further questions, please ensure you are using the latest versions of torch
and ultralytics
packages. You can update them using:
pip install --upgrade torch ultralytics
Feel free to reach out if you need any more assistance. Happy validating! 😊
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👋 Hello @yasinda-s, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.
If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.
Join the vibrant Ultralytics Discord 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.
Install
Pip install the ultralytics
package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.
pip install ultralytics
Environments
YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
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