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  • YOLOv5 – Optimization of training data for PFM-1 antipersonnel mine detection
    After training the first 31 models for anti-personnel mine detection, my data set has grown significantly. The goal was to reduce the false positives. This was because there were always leaves detected that were similar to a PFM-1 anti-personnel mine and were falsely detected as such. In order for the YOLOv5 network to learn that […]
  • YOLOv5 – Training a neural network for PFM-1 antipersonnel mine detection
    This paper is about training a neural network based on YOLOv5. This YOLO network should be able to recognize PFM-1 anti-personnel mines in order to support the automated detection of these mines. Since this is my first time working with YOLOv5 and synthetic data, I am curious about the results that a validation dataset will […]
  • Stable Diffusion – AUTOMATIC1111 Experts configuration
    Now I have reached a point in the use of Automatic1111 that I also go a little deeper into the configuration. At the beginning I had passed parameters at the start of the software like listen or port. But there are much more possibilities to configure the own hardware with Automatic1111. The configurations I have […]
  • Stable Diffusion – Dreambooth Training Finetuning Run Part 2/2
    After you have added the Dreambooth extension to your AUTOMATIC1111 system, I would like to briefly explain how to set up and start a training. Since I’m still learning and trying out myself, this tutorial should be understood as a first idea of how the processes are and what you have to pay attention to. […]
  • Stable Diffusion – AUTOMATIC1111 Ubuntu installation part 2/2
    After the first part of the installation instructions all prerequisites were created, the second part of the instructions now follows the installation of Automatic1111. This works so well that I myself was very surprised how easy it was. It is important that you do the installation in the active Anaconda environment “stable-diffusion”. If you don’t […]

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