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Slice COCO dataset for varying image sizes

Source: R/dataset.R
slice_dataset.Rd

Uses SAHI to slice images and annotations into tiles. Images smaller than slice_size are treated as single slices (no fragmentation). Larger images are split into overlapping tiles, which increases training samples and improves detection of small objects in large images.

Usage

slice_dataset(
  input_dir,
  output_dir,
  slice_size = 1024,
  overlap = 0.2,
  min_area_ratio = 0.1,
  output_format = ".jpg"
)

Arguments

input_dir

Input directory with train/ and valid/ subdirectories containing COCO annotations. If a test/ directory exists, it will also be sliced.

output_dir

Output directory for sliced dataset (will be created)

slice_size

Slice size in pixels (default: 1024). Images smaller than this will not be fragmented.

overlap

Overlap ratio between adjacent slices (default: 0.2, or 20%)

min_area_ratio

Minimum area ratio to keep object fragments (default: 0.1). Objects cut by slice boundaries with less than 10% visible area are dropped.

output_format

Output image format: ".jpg" or ".png" (default: ".jpg"). JPG minimizes storage (~10x smaller) with minimal quality loss. Use PNG for lossless slicing.

Value

Path to sliced dataset directory (invisibly)

Details

This is particularly useful for dense detection datasets with varying image sizes. For the inclusions dataset, slicing with slice_size = 1024 will:

  • Keep small images (<1024px) intact as single slices

  • Split large images (>1024px) into 2-4 overlapping tiles

  • Result: ~2x more training images with better small object coverage

Examples

if (FALSE) { # \dontrun{
# Slice dataset for training
sliced_dir <- slice_dataset(
  input_dir = "data/processed/inclusions",
  output_dir = "data/processed/inclusions_sliced",
  slice_size = 1024,
  overlap = 0.2
)

# Train on sliced dataset
train_model(data_dir = sliced_dir, ...)
} # }