ValueError: not enough values to unpack (expected 2, got 1)：————out, train_out = model(im) if compute_loss else (model(im, augment=augment), None)

[JiannBai]

Search before asking

• I have searched the YOLOv5 issues and discussions and found no similar questions.

Question

 When I was training my own dataset using train.py scripts, this error occurred during the validation phase：out, train_out = model(im) if compute_loss else (model(im, augment=augment), None) ；ValueError: not enough values to unpack (expected 2, got 1)。
This is the code of the val.py that reported the error：

# Inference with dt[1]: out, train_out = model(im) if compute_loss else (model(im, augment=augment), None)

Why return one less value?

Additional

No response

[github-actions]

👋 Hello @jianbaiwuqing, 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.

[JiannBai]

I changed the return value of forword method of the Detect class in yolo.py with a little change：
turn return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x) into return x if self.training else (torch.cat(z, 1),x) if self.export else (torch.cat(z, 1), x)
That's okay, but I'm not quite sure if I'm correctly modifying it

[glenn-jocher]

@jianbaiwuqing I've removed the --train argument from export.py to avoid confusion in #9429

In general all exports are intended for inference-only deployments, so I think this will make things simpler for users.

[github-actions]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

Access additional YOLOv5 🚀 resources:

• Wiki – https://github.com/ultralytics/yolov5/wiki
• Tutorials – https://docs.ultralytics.com/yolov5
• Docs – https://docs.ultralytics.com

Access additional Ultralytics ⚡ resources:

• Ultralytics HUB – https://ultralytics.com/hub
• Vision API – https://ultralytics.com/yolov5
• About Us – https://ultralytics.com/about
• Join Our Team – https://ultralytics.com/work
• Contact Us – https://ultralytics.com/contact

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 YOLOv5 🚀 and Vision AI ⭐!