Benchmarks for AutoAlbument - AutoML for Image Augmentation.
CIFAR-10 (Classification)
Augmentation strategy
Top-1 Accuracy
Top-5 Accuracy
Baseline
91.79
99.63
AutoAlbument
96.02
99.91
Augmentation strategy
Top-1 Accuracy
Top-5 Accuracy
Baseline
98.31
99.68
AutoAlbument
98.48
99.72
ImageNet (Classification)
Model: ResNet-50.
Baseline augmentation strategy:
Resize an image to 256x256 pixels.
Crop a random 224x224 pixels patch.
Apply Horizontal Flip with probability 0.5.
AutoAlbument augmentation strategy:
Resize an image to 256x256 pixels.
Crop a random 224x224 pixels patch.
Apply AutoAlbument augmentation policies.
Configs: AutoAlbument augmentation search | Baseline training | AutoAlbument training .
Augmentation strategy
Top-1 Accuracy
Top-5 Accuracy
Baseline
73.27
91.64
AutoAlbument
75.17
92.57
Pascal VOC (Semantic segmentation)
Model: DeepLab-v3-plus.
Baseline augmentation strategy:
Resize an image preserving its aspect ratio, so the longest size is 256 pixels.
If required, pad an image to the size 256x256 pixels.
Apply Horizontal Flip with probability 0.5.
AutoAlbument augmentation strategy:
Resize an image preserving its aspect ratio, so the longest size is 256 pixels.
If required, pad an image to the size 256x256 pixels.
Apply AutoAlbument augmentation policies.
Configs: AutoAlbument augmentation search | Baseline training | AutoAlbument training .
Augmentation strategy
mIOU
Baseline
73.34
AutoAlbument
75.55
Model: DeepLab-v3-plus.
Baseline augmentation strategy:
Resize an image preserving its aspect ratio, so the longest size is 256 pixels.
If required, pad an image to the size 256x256 pixels.
Apply Horizontal Flip with probability 0.5.
AutoAlbument augmentation strategy:
Resize an image preserving its aspect ratio, so the longest size is 256 pixels.
If required, pad an image to the size 256x256 pixels.
Apply AutoAlbument augmentation policies.
Configs: AutoAlbument augmentation search | Baseline training | AutoAlbument training .
Augmentation strategy
mIOU
Baseline
79.47
AutoAlbument
79.92
How to run the benchmarks
Download datasets and put them in the following directory structure:
Clone this repository.
Run the run.sh
script that will build a Docker image and train models using the following command:
./run.sh </path/to/data/directory> </path/to/outputs/directory>
e.g.
./run.sh ~/data ~/outputs
where
</path/to/data/directory>
is a path to a directory that contains datasets (e.g., a directory that contains folders imagenet
, pascal_voc
, etc)
</path/to/outputs/directory>
is a path to a directory that should contain outputs from a training pipeline, such as a CSV log with metrics and a checkpoint with the best model.