Yolov5: How to visualize input Dataloader after Augmentation?

[chokyungjin]

Hi! I have a question your Dataloader.
Your yolov5 is soo difficult to me to analyze..
How to visualize input Dataloader after Augmentation?
Before I training, I wonder how images come out in the dataloader.

Thanks!

[Ownmarc]

Trust me, Glenn’s code is really easy to read and understand compared to many repo in ML.

You can check the images created in the folder ‘’runs/exp0/‘’ they will show you 3 first batches when starting a training. Else you could simply create a dataloader object in a notebook or something using the repo’s code and load image one after an other and view them however you want by writing to disk or showing them with pyplot

[DacDinh147]

You have to look in the LoadImagesAndLabels(Dataset) class. This class return a tuple (torch.from_numpy(img), labels_out, self.img_files[index], shapes).

Here torch.from_numpy(img) is a tensor with shape [batch size, channels, w, h] and labels_out is a list of item. Each item of labels_out represents a bounding box annotation for each image and is a list of bounding box (x_center, y_center, w, h) corresponding to the object in the specific image. Because the bounding box coordinates (x_center, y_center, w, h) was normalized, so you have to multiply it with the width and height of the image to visualize it xc, yc, w, h = xc*w_img, yc*h_img, w*w_img, h*h_img

Visualization with batch size=6

[glenn-jocher]

We display this clearly in the notebook, suggest you start from there:
https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb#scrollTo=DLI1JmHU7B0l

[github-actions[bot]]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.

[buimanhlinh96]

Hi @glenn-jocher, how can i add albumentations in torch library for augmentations in DataLoader?

[glenn-jocher]

@buimanhlinh96 you can customize the trainloader here:
https://github.com/ultralytics/yolov5/blob/7220cee1d1dc1f14003dbf8d633bbb76c547000c/utils/datasets.py#L328

[Auth0rM0rgan]

@glenn-jocher, Could you please provide more details that how to use albumentations in the DataLoader??

[glenn-jocher]

@Auth0rM0rgan we don't have an integration with albumentations. YOLOv5 augmentation hyperparameters are set here:
https://github.com/ultralytics/yolov5/blob/f6b3c96966d963a4c69736c8691f068e5b554247/data/hyp.scratch.yaml#L22-L33

You are free to modify the dataloader also as you see fit here:
https://github.com/ultralytics/yolov5/blob/7220cee1d1dc1f14003dbf8d633bbb76c547000c/utils/datasets.py#L328

[Auth0rM0rgan]

Hey @glenn-jocher, Thanks for the quick reply!

I know about the YOLOv5 augmentation but I'm trying to add some blur augmentations that are not in the yolov5 augmentation such as MotionBlur, Gaussian Blur, and ... but I have difficulty to add these augmentations inside YOLOv5. I will appreciate if you can help me even a little...

Gracias!

[glenn-jocher]

@Auth0rM0rgan ah I see. Well you can access the raw image as a numpy array img here after it's already passed through the YOLOv5 augmentations, so I would just insert any augmentations here. Be careful to correctly modify labels correspondingly as well if your augmentations require it though.
https://github.com/ultralytics/yolov5/blob/f6b3c96966d963a4c69736c8691f068e5b554247/utils/datasets.py#L526-L528

[Auth0rM0rgan]

@glenn-jocher Thanks! made it work. Now I can apply a set of different kinds of augmentations at pixel-level and spatial-level!

[glenn-jocher]

@Auth0rM0rgan hey cool! Maybe you could consider submitting a PR for general Albumentations integration? I know its a popular tool so others might find that useful.

[Auth0rM0rgan]

@glenn-jocher, Sure! will do it during the weekend!