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2 years ago 👋 Hello @AchiadChenzi, 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:
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # installEnvironments
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
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glenn-jocher
AchiadChenzi
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Description
I train Yolov5 in Google Colab and want to ignore anomaly. For instance, lets assume that anomaly is until 5 frames. So I need that the result will ignore those 5 frames and label them as the rest of the frames. for example: the program labels: 1,1,1,1,1,1,1,2,2,1,1,1,1,1,1,1. will ignore those '2' label and label them as '1'.
Use case
In my validation I've got only 80% and I want that the program will ignore those anomaly. I cant improve the training because those 2 labels in my train are very close to each other(look almost the same).
Additional
No response
Are you willing to submit a PR?