• DepthAI-v2

  • Custom data in YOLOV7 + No pre-training for simple detection

Hello,

I've been using the old/deprecated DepthAI, but it is no longer usable. Therefore, I have switched to the YOLOV7, but I have some questions about it all :

  1. Where am I supposed to put the dataset? In the tutorial, the line above says "yolov7". Am I supposed to put the dataset folder inside "yolov7", or in the "content"/root folder?
  1. Is it possible to not use a pre-trained version? If so, what would be the steps? I only want to create a model for simple plank (wood) detection. I don't need the model to recognize anything else.

Have a nice day!

    jakaskerl

    Hello!

    I apologize about my lack of precision earlier. I was in fact, talking about the notebook you sent. I've been using it with custom data. In this graph :

    It's expected to put custom images with their respective labels. Am I supposed to create the folders in the root/"content" folder, or in the yolov7 folder that the notebook creates?

    Concerning the custom model, I am pretty new to machine learning. I'm simply looking to create a model that will detect passing wood planks. Won't the extra items (face, books, etc.) try to be detected, resulting in more CPU utilization? I'm looking to use the model on a Raspberry Pi, I don't have that many resources in terms of CPU and RAM.

    Thanks a lot for the help and have a nice day!

        Hi FrancisGuindon
        The stucture looks to me like "." (base directory - dot signifying current directory) is called yolov7. yolov7 then includes dataset folder with the rest of the tree. In short, put dataset inside the yolov7.

        I too am not very familiar with this style of training so I did some digging around. Yes, you can train your own model from scratch. The notebook seems like it's only fine-tuning the model with new classes. I believe you can train a model from scratch by calling: --weights '' when running the train.py (the notebook specifies the weights from a downloaded pre-trained model, but you don't need to)

        Let me know how it goes.

        Hope this helps,
        Jaka

            jakaskerl

            Thank you so much! Everything makes a lot more sense.

            My last remaining question is about the voc.yaml file it creates. It contains the path for the training pictures and labels. It points to the images/labels that the notebook downloads. Also, it seems to not mention fetching into the custom dataset folder during the execution of train.py (only the images that the notebook give). Should I change the paths inside the voc.xaml to the paths where my custom dataset is?

            If you don't have the answer, I'll experiment with some on my own. I simply feel that the old tutorial was clearer on how custom data was used, and was more user-friendly.

            Thanks for your time and have a nice day!
            -Francis

                Hi FrancisGuindon
                Yes, I believe you need to switch the path to point to your own dataset.

                What i think they are doing in the notebook is adding the VOC dataset to the already existing yolov7 dataset. You would need to switch the VOC with your own and add it to empty yolo model (one that doesn't use the checkpoint weights).

                Hope it helps,
                Jaka