Lightly uses its data selection technology to compare against random and other subsampling methods on well-known academic datasets. We make the filenames of the samples in the curated datasets available here, for free, so that everyone can use the improved datasets for their own applications.
Note: We only run the training data through our data selection solution. The test set stays the same.
Task: Object Detection (7 classes)
Total dataset: 7481 images
Training set: 5984 images
Curated training set (90%): 5386 images
Validation set: 1497 images
Using Lightly we can save 10% of the data labeling costs while improving the model accuracy!
Learn more about how the Lightly subsampling method compares against random subsampling on well-known acadmic datasets.
Note: We only run the training data through our data selection solution. The test set stays the same. We do not recommend to do this in practice since train / test should have a similar distribution to properly evaluate a ML model. Additionally, these datasets went through a manual cleaning procedure to balance the dataset. We see on customer data much stronger impacts. Typically, we see the same test accuracy with 50% of the training data selected by Lightly as when using the full training dataset.