Comments (3)
👋 Hello @berinbalci, 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.
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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.
from ultralytics.
@berinbalci hello,
Thank you for reaching out! To analyze the confusion matrix and calculate the true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN), you can follow these steps:
- True Positives (TP): The number of correct predictions that an instance is positive.
- True Negatives (TN): The number of correct predictions that an instance is negative.
- False Positives (FP): The number of incorrect predictions that an instance is positive.
- False Negatives (FN): The number of incorrect predictions that an instance is negative.
In the context of object detection, these values are derived from the bounding box predictions compared to the ground truth boxes.
Here's a simple way to calculate these values using the confusion matrix:
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# Example confusion matrix
confusion_matrix = np.array([[50, 10], [5, 35]])
# Extracting TP, TN, FP, FN
TP = confusion_matrix[1, 1]
TN = confusion_matrix[0, 0]
FP = confusion_matrix[0, 1]
FN = confusion_matrix[1, 0]
print(f"True Positives (TP): {TP}")
print(f"True Negatives (TN): {TN}")
print(f"False Positives (FP): {FP}")
print(f"False Negatives (FN): {FN}")
# Visualizing the confusion matrix
sns.heatmap(confusion_matrix, annot=True, fmt='d', cmap='Blues')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.show()
If you are using the latest version of the ultralytics
package, you can generate and visualize the confusion matrix directly using the model.val()
method, which will save the confusion matrix as an image file. This can help you better understand the performance of your model.
For more detailed information on performance metrics and how to interpret them, you can refer to our Performance Metrics Guide.
If you encounter any issues or need further assistance, please ensure you are using the latest versions of torch
and ultralytics
. If the problem persists, kindly provide a minimum reproducible example as outlined here so we can investigate further.
Best regards and happy coding! 😊
The Ultralytics Team
from ultralytics.
👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.
For additional resources and information, please see the links below:
- Docs: https://docs.ultralytics.com
- HUB: https://hub.ultralytics.com
- Community: https://community.ultralytics.com
Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!
Thank you for your contributions to YOLO 🚀 and Vision AI ⭐
from ultralytics.
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