Labelling Objects Occluded objects in Extreme Environment

Avv22 commented 5 months ago

Search before asking

[X] I have searched the YOLOv5 issues and discussions and found no similar questions.

Question

I am dealing with object detection of objects with similar color that are occlude each other due to the manufacturing nature. I am trying to get multiple view of the object occluded from 3 directions to alleviate this problem. I am not sure if suggest an alternative solution?

Additional

No response

github-actions[bot] commented 5 months ago

👋 Hello @Avv22, 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 a minimum reproducible example to help us debug it.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.

Requirements

Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. 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):

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, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

Introducing YOLOv8 🚀

We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!

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 commented 5 months ago

Hello!

Thank you for reaching out with your query. Handling occluded objects, especially in environments where they share similar colors, can indeed be challenging. Utilizing multiple views as you suggested is a practical approach. This method can significantly enhance the model's ability to discern between overlapping objects by providing different perspectives.

Another strategy you might consider is augmenting your training dataset with synthetic data that simulates occlusions from various angles. This can help the model learn to recognize partially visible objects more effectively.

Additionally, experimenting with different anchor box sizes or aspect ratios could also prove beneficial, as it might help the model to better fit the specific shapes and sizes of the occluded objects in your dataset.

If you haven't already, tuning the hyperparameters specifically for handling occlusions might also yield better detection results.

Best of luck with your object detection project, and please keep us posted on your progress!

Avv22 commented 5 months ago

Thank you. Do I need to rotate image and to deform it in the training dataset or this is done internally as part of YOLOv5 and YOLOv8 versions please?

glenn-jocher commented 5 months ago

Hello!

Great question! YOLOv5 and YOLOv8 inherently support various data augmentation techniques, including random rotations and deformations (like affine transformations). These augmentations are applied automatically during training to enhance the model's robustness and generalization capabilities. So, there's no need for you to manually rotate or deform images in your dataset unless you have specific augmentation needs beyond what's already provided.

Happy training! 🚀

github-actions[bot] commented 4 months ago

👋 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 ⭐

ultralytics / yolov5