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Once all images have been labelled, navigate to Dataset in the left bar and ensure that the split is as close to 70/20/10 as possible (a few images of variance is fine). Then, navigate to Generate. Here, you will create a "Version" of the dataset.
Currently, here are the settings we use:
Generate an augmented version:
Leave default
Leave default
None
Flip, Hue, Saturation, Brightness:
(2022-2023 used the following:)
Flip: Horizontal, Vertical
Hue: -27° and +27°
Saturation: -75% and +75%
Brightness: -25% and +0%
3x (but if a higher multiplier is available, use it)
Click Generate (it takes a few minutes for the version to be generated)
To download a dataset version, see Dataset Download and Usage.
Note: Here are the settings used in 2023 for the competition:
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We do not know how well these augmentations worked for the team, since this wasn’t fully documented. Henceforth, we would like to experiment with various augmentations and document our results.
Suggestions for Data Augmentation
Roboflow documentation (https://docs.roboflow.com/datasets/image-augmentation) suggests that changing the exposure, blur and noise be used with the augmentations used in 2023. They also have a detailed video on data augmentation for aerial data (https://blog.roboflow.com/image-augmentations-for-aerial-datasets/).
If we have an excellent camera, noise may not be an issue, but a little blur could help replicate the quality of the drone’s camera. Blur would be useful to simulate conditions where objects are unclear from higher altitudes. As a reference point, if the dataset images are of identical quality to the landing pad images in Landing Pad Data Labelling Instructions, then blur and noise should be incorporated.
Roboflow also suggests cropping (https://blog.roboflow.com/why-and-how-to-implement-random-crop-data-augmentation/) to help train the model in identifying various objects at different zooms (this includes cutting out part objects such as landing pads). We do not want partially cut images of these objects, so this augmentation is a no-go for now (if we have time, we could try to train a model capable of detecting sections of landing pads, but this may be unnecessary).
Use the 14-day free premium plan (no credit card needed) and we can use 5x instead of 3x as a dataset multiplier when creating our augmented dataset.
Augmentations to Experiment With
Note: update this list of augmentations when the ranges below are decided.
Augmentation 1 (replicating augmentations from 2023):
Flip: Horizontal, Vertical
Hue: -27° and +27°
Saturation: -75% and 75%
Brightness: -25% and 0%
Augmentation 2:
Flip: Horizontal, Vertical
Hue: -27° to +27° [range]
Saturation: -75% to 75% [range]
Brightness: -25% to 0% [range]
Blur and Noise
Augmentation 3:
Flip: Horizontal, Vertical
Hue: -27° to +27° [range]
Saturation: -75% to 75% [range]
Brightness: -25% to 0% [range]
Crop, Blur and Noise
Past Data Augmentation Tasks
The Data Augmentation task from early 2024 can be found at 2024-04-03 Dataset Augmentation.