Precision and recall values returns 0 for every epochs

[swaminathanayyappan190799]

hi , with the latest code of yolov5. Whenever I did the training for a custom dataset it returns the precision and recall values as 0

[github-actions]

👋 Hello @swaminathanayyappan190799, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.

For business inquiries or professional support requests please visit https://ultralytics.com or email support@ultralytics.com.

Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab and Kaggle 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 YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.

[arun-gautham]

hi,

Same here , this was same data set that was trained same day back and the values were coming ( validation precision and recall ) now they don't seem to come or are always 0

[pourmand1376]

How is the distribution of your classes? Is your dataset imbalanced?

[swaminathanayyappan190799]

@pourmand1376, there is a slight imbalance in the classes but eventhough the previous releases of yolov5l have handled these issues perfectly with the current dataset. The problem is actually with the latest build

[pourmand1376]

Hmm. So you can easily switch to the release that you want and use it.

This is very easy with git. Just use git switch.

[arun-gautham]

!git checkout d1dfcab

did the job

[swaminathanayyappan190799]

@pourmand1376 , it worked thanks for your suggestion

[glenn-jocher]

@swaminathanayyappan190799 I've reproduced this issue and am working on it now.

[glenn-jocher]

First commit producing zero output seems to be this, which is really strange as it doesn't affect Detection training.
840b723

[glenn-jocher]

@pourmand1376 @swaminathanayyappan190799 @arun-gautham good news 😃! Your original issue may now be fixed ✅ in PR #9037. To receive this update:

• Git – git pull from within your yolov5/ directory or git clone https://github.com/ultralytics/yolov5 again
• PyTorch Hub – Force-reload model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
• Notebooks – View updated notebooks
• Docker – sudo docker pull ultralytics/yolov5:latest to update your image

Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!

[swaminathanayyappan190799]

Thank you @glenn-jocher, it was resolved with the latest committed code we have checked it

[robotaiguy]

Ohhh, whewww...no cause for alarm, it was just the boss diddling with the production code again. :P
Sorry for the laugh, @glenn-jocher but this was classic.