return and saved results as detections crops using results.crop(save=True)

[hulkds]

Search before asking

• I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

Detection

Bug

Bug when doing results.crop(save=True) after load yolov5 via pytorch hub.

Bug message:

# b = xyxy2xywh(xyxy.view(-1, 4))  # boxes
AttributeError: 'list' object has no attribute 'view'

Bug found in this line

Solution:

b = xyxy2xywh(torch.stack(xyxy).view(-1, 4))  # boxes

Environment

• YOLOv5 🚀 2023-8-2 Python-3.10.12 torch-2.0.1+cu117 CUDA:0 (NVIDIA GeForce GTX 1650 Ti, 3904MiB)
• Ubuntu 22.04
• Python3.10.12

Minimal Reproducible Example

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

# Image
im = 'https://ultralytics.com/images/zidane.jpg'

# Inference
results = model(im)

# cropped detections dictionary
crops = results.crop(save=True)  

Additional

Consider using BGR=True if the crops colors is not what you want.

crops = results.crop(save=True, BGR=True) 

Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[github-actions]

👋 Hello @hulkds, 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.

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Requirements

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

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cd yolov5
pip install -r requirements.txt  # install

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Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.

Check out our YOLOv8 Docs for details and get started with:

pip install ultralytics

[glenn-jocher]

@hulkds this is a known bug when using results.crop(save=True) after loading YOLOv5 via PyTorch Hub. The error is caused by a line in the code that expects a tensor but receives a list.

To address this issue, you can modify the code at line 296 in ultralytics/utils/plotting.py as follows:

b = xyxy2xywh(torch.stack(xyxy).view(-1, 4))  # boxes

This change will convert the list of bounding boxes into a tensor before passing it to the xyxy2xywh function.

Thank you for bringing this to our attention, and we appreciate your willingness to submit a PR! Your contribution is valuable to the YOLOv5 community.

[hulkds]

@glenn-jocher I faced a CI error:
E TypeError: stack(): argument 'tensors' (position 1) must be tuple of Tensors, not Tensor

To address this issue, I tried to pass a tuple in torch.stack() and it works

b = xyxy2xywh(torch.stack((xyxy)).view(-1, 4))  # boxes

I'll rerun the Ultralytics CI workfow and will submit the PR again.

[hulkds]

@glenn-jocher I have created a PR: ultralytics/ultralytics#4124

[glenn-jocher]

@hulkds thank you for creating the pull request! We appreciate your contribution to the YOLOv5 repository. Our team will review it and provide feedback as soon as possible. Keep up the good work!

[glenn-jocher]

@hulkds ok between ultralytics/ultralytics#4127 and #11936 I think this bug should be resolved.

Please update YOLOv5 dependencies with pip install -r requirements.txt and try again, thank you!

[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.

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