Confusion Matrix Missing False Negatives

jbutle55 commented 2 years ago

Search before asking

[X] I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

Validation

Bug

In val.py, during the "Evaluate" stage, a single batch worth of metrics for the confusion matrix is computed using:

                if plots:
                    confusion_matrix.process_batch(predn, labelsn)

However, if the length of predictions for the image in question is zero, this portion of code is skipped over due to:

            if len(pred) == 0:
                if nl:
                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
                continue

If this continue statement is called then this batch is not processed for the confusion matrix, but if the relevant image had ground truth objects, meaning these were missed detections since len(pred) was 0, then these FNs won't be accounted for in the confusion matrix.

Environment

No response

Minimal Reproducible Example

No response

Additional

No response

Are you willing to submit a PR?

[X] Yes I'd like to help by submitting a PR!

github-actions[bot] commented 2 years ago

👋 Hello @jbutle55, 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.

glenn-jocher commented 2 years ago

@jbutle55 hi, thank you for your feature suggestion on how to improve YOLOv5 🚀!

The fastest and easiest way to incorporate your ideas into the official codebase is to submit a Pull Request (PR) implementing your idea, and if applicable providing before and after profiling/inference/training results to help us understand the improvement your feature provides. This allows us to directly see the changes in the code and to understand how they affect workflows and performance.

Please see our ✅ Contributing Guide to get started.

jbutle55 commented 2 years ago

This got merged, closing the issue.

glenn-jocher commented 10 months ago

Thank you for your contribution, @jbutle55! Your assistance is greatly appreciated. 🎉 We're grateful for your help in enhancing YOLOv5, and we look forward to any future contributions you may make. If you have any more ideas or feedback, feel free to share them!

ultralytics / yolov5