Penguins_object_detection.ipynb

Hello Lihi, Thanks for sharing! My question is if the final number of classes in your dataset after implementing transfer learning is 81 including penguin?

Hello, Thank you for clarification. I was thinking that by using --weights 'yolov5s6.pt' will end-up enhancing capability of additional coco classes.

Hi Lihi, Thanks for the code, I just to ask did you add the photos where penguins are not present and if yes then where did you put in the folder structure and the label file.

I did had photos without penguins, it is recomadad up to 10%. Just put them in the same images folder, and without a label file.

Thank you so much for replying, great help, I will definitely do that.

Love to help, good luck!

Hi Lihi, thank's for you contributions.

How i can change "pinguin" to... subjetcs or objects? weapons for example? What i need to change?

Thanks and best regards.

Samuel.

Hi Lihi

Thanks for an excellent acritical. Very helpful.

I've used your code as is on Colab with 300 images of penguins except for changing 'hyp.finetune.yaml' to 'hyp.VOC.yaml' as 'hyp.finetune.yaml' doe not exist anymore.

Here is my Precision Recall Curve, any idea why its so bad compared to your graph.
I got the images from Google OpenImages and used Roboflow to split it into 70% train 20% test 10% valid.

Regards
Christoffel

great

Hi there, great tutorial; I was wondering if was possible to share the roboflow repository too?

Thanks!

Hi, great tutorial. I have chosen Yolov5s6 model for training my data. My results for inferences didn't come out as I expected. Each image was detected 50% sth on average. I annotated 1274 images. Do you think I should've selected more images? Does this result have to do with my number of inputs?

Hi, Lihi! Where do I get the requirements.txt file?

The requirements.txt file is in the yolov5 repo.

Hi. Where is the 'hyp.finetune.yaml'? Does it not exist anymore? Should I use something else like 'hyp.VOC.yaml'. I believe I will have to decrease the learning rate further.

Hello,
I am training also for a single class but the obj_loss keeps going up. Do you know why? what should i do?

In addition, I am not sure I understand the correlogram images. Any clue why this look like this? I am trying to detect an specific type of flower.

where is all the data now? I'm trying to reproduce this and there is no images nor .jaml file

Thanks for this tutorial!
Do the input images need to have the expected size (1280x1280)? If they are a different size, will they be rescaled?

Hi @eribnick,
I'm glad you found the tutorial helpful. The detector was optimized for that specific input size, but you can use any other input size. The images are rescaled before entering the network. You can choose the image size to which you want to rescale your images using the --img parameter.
Good luck!

Changes

Hello @Lihi-Gur-Arie, thanks for the tutorial it helped me understand, how to use yolo. One thing I'd like to understand better would be, let's say I have 5000 images of same class, do I need to manually label all 5k images, or labeling let's say 10% is enough? ( I know there are other factors which can effect that percentage, I just want to know if it's doable with 10% or not)

Hello @Lihi-Gur-Arie, Thank you very much for this tutorial.
Could you please provide some information about the sizes of the training, validation and testing sets?
Thanks again, Good Luck!