model evaluation about ultralytics

[github-actions]

👋 Hello @money1231, 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.

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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.

[glenn-jocher]

@money1231 hi there! 😀 Great job on achieving an 80% mAP50 with your yolov8s-seg model! Here are a few suggestions to possibly boost your performance closer to that 95% goal:

1. Augmentation and Additional Data: You're correct that expanding your dataset can help. Additionally, consider using more robust data augmentation techniques if you haven’t maximized this yet. This can include variations in lighting, cropping, and adding noise.

2. Model Variation: Experimenting with different model sizes could yield improvements. Larger models (e.g., yolov8m, yolov8l) might capture finer details but require more computational resources. It's a balance between performance and resource availability.

3. Addressing False Positives: Enhancing your training set with more examples that are similar to your false positives can help the model learn to distinguish better. Additionally, fine-tuning the confidence threshold and leveraging non-maximum suppression (NMS) parameters can effectively reduce false positives.

Here’s a brief example on adjusting the confidence threshold and NMS:

from ultralytics import YOLO

model = YOLO('yolov8s-seg.pt')
# Adjust these parameters based on your validation performance
model.conf = 0.5  # Confidence threshold
model.iou = 0.5  # Intersection Over Union threshold for NMS

Training for more epochs can sometimes lead to improvements, but keep an eye out for signs of overfitting. Regular validations can guide you here.

Balancing your dataset class distribution could also be beneficial, as disparity in class instances can sometimes bias the model.

Remember, incremental changes and validations are key ✔️. Keep experimenting, and good luck! 🚀

[money1231]

Can you please suggest if its overfitting based on the loss metrics. I think I need to train it more?

[glenn-jocher]

@money1231 Hey there! 🌟 Based on what you've described, it sounds like you're pondering over the possibility of overfitting due to your model's loss metrics. To really tell if it's overfitting, typically you'd compare the training loss with the validation loss. If your training loss is consistently decreasing but your validation loss starts to increase or fluctuates significantly, that's a classic sign of overfitting.

Training more might not always be the solution if you're facing overfitting. Instead, consider techniques like adding dropout layers, using data augmentation, or even trying out early stopping. Each of these methods can help your model generalize better to unseen data.

Here's a tiny nugget on early stopping with a hypothetical EarlyStopping class:

from ultralytics import YOLO, EarlyStopping

model = YOLO('model.yaml')
early_stopping = EarlyStopping(patience=10, verbose=True)

for epoch in range(100):
    train_loss = model.train(...)  # Your training logic here
    val_loss = model.val(...)      # Your validation logic here
    
    # Check early stopping condition
    if early_stopping.check(val_loss):
        print("Stopping early!")
        break

This little example stops the training process if the validation loss doesn't improve after a certain number of epochs (patience parameter).

Remember, understanding your loss trends is crucial and adjusting your training strategy accordingly can help optimize performance. Keep experimenting! 🔍

[github-actions]

👋 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 ⭐