Image segmentation for yolo

Question

For a project I am using YOLO to detect phallusia (microbial organisms) that swim into focus in a video. The issue is that I have to train YOLO on my own data. The data needs to be segmented so I can isolate the phallusia. I am not sure how to properly segment/cut-out the phallusia to fit the format that YOLO needs. For example in the picture below I want YOLO to detect when a phallusia is in focus similar to the one I have boxed in red. Do I just cut-out that segment of the image and save it as its own image and feed to that to YOLO? Do all segmented images need to have the same dimensions? Not sure what I am doing and could use some guidance.

Answer 1

It looks like you need to start from basics, ok, no fear. I will try to suggest a simple route to start efficiently to use YOLO techniques. Luckly the web has a lot of examples.

1. Understand WHAT is a YOLO method.
   Andrew NG's YOLO explanation is a good start, but only if you alread know what are classification and detection.
2. Understand the YOLO Loss function, the heart of the algorithm.
   Check the paper YOLO itself, don't be scared. At page #2, in Unified Detection section, you will find the information about the bounding box detection used, but be aware that you can use whatever notation you want (even invent a new one), in order to be compatible with the Loss function, real meaning of this algorithm.
3. Start to implement an example
   As I wrote above, there are plenty of examples. You can check this one if you are familiar with python and tensorflow. Inside it you will find a way to prepare the dataset, that is your target for this question, I think. In this case a tool named labelImg is used.

I hope it will be useful. Please share your code when it will be ready, I'm curious :). Good luck!