Skip to content

Augmentations

RF-DETR uses torchvision-native default augmentations for training, validation, prediction, and export preprocessing. Omitting aug_config uses the default training augmentation stack: resize/crop scale jitter plus horizontal flip at 50%. Passing aug_config={} disables the horizontal flip while keeping required resizing and normalization.

RF-DETR also supports advanced custom data augmentations via optional Albumentations, with automatic bounding box and mask handling for geometric transforms. Albumentations 1.4.24+ and 2.x are supported.

Augmentation Backend Values

augmentation_backend (on model.train()/TrainConfig) selects which pipeline runs the augmentation stack:

Value Behavior
"cpu" (default) Auto-picks the best installed CPU backend: Albumentations > Kornia (CPU) > torchvision.
"auto" Same priority as "cpu", but prefers GPU-side Kornia first when CUDA is available.
"torchvision" Forces the torchvision-native default pipeline, never auto-upgraded — use to pin behavior regardless of what's installed.
"albumentations" Forces the CPU Albumentations pipeline; requires rfdetr[augment].
"kornia" Forces the GPU Kornia pipeline; requires rfdetr[augment].

"cpu"/"auto" resolution is late (checked at dataset-build time, not when TrainConfig is constructed), so a saved config stays portable across environments with different optional packages installed. The legacy strings "tv", "albu", and "gpu" are still accepted as aliases for "torchvision", "albumentations", and "kornia" respectively.

Quick Start

Install the optional augmentation extra before using custom aug_config dictionaries or built-in Albumentations presets:

pip install "rfdetr[train,augment]"

Then pass aug_config to your training call. Import one of the built-in presets:

from rfdetr import RFDETRSmall
from rfdetr.datasets.aug_configs import AUG_CONSERVATIVE, AUG_AGGRESSIVE, AUG_AERIAL, AUG_INDUSTRIAL

model = RFDETRSmall()
model.train(dataset_dir="path/to/dataset", epochs=100, aug_config=AUG_CONSERVATIVE)

Or pass a custom dict directly — keys are Albumentations transform names:

model.train(
    dataset_dir="path/to/dataset",
    epochs=100,
    aug_config={
        "HorizontalFlip": {"p": 0.5},
        "Rotate": {"limit": 15, "p": 0.3},
        "GaussianBlur": {"p": 0.2},
    },
)

To disable all optional training augmentation including the torchvision default horizontal flip: aug_config={}.

aug_config controls only the augmentation stack — the torchvision-native default, or the Albumentations/Kornia stack when a non-empty aug_config is passed. To also disable the independent resize → crop → resize branch (Option B) in the training resize pipeline — so no spatial clipping occurs and annotations near image borders stay intact — pass scale_jitter=False to model.train().

Built-in Presets

Preset Best for
AUG_CONSERVATIVE Small datasets (under 500 images)
AUG_AGGRESSIVE Large datasets (2000+ images)
AUG_AERIAL Satellite / overhead imagery
AUG_INDUSTRIAL Manufacturing / inspection data

All presets are Albumentations config dicts and require rfdetr[augment]. They are plain dicts, so you can inspect or extend them before passing:

from rfdetr.datasets.aug_configs import AUG_AGGRESSIVE

my_config = {**AUG_AGGRESSIVE, "VerticalFlip": {"p": 0.1}}
model.train(dataset_dir="...", aug_config=my_config)

Recommendations by Dataset Size

Dataset Size Recommended preset
Under 500 images AUG_CONSERVATIVE — flip + mild brightness/contrast
500–2000 images Default or AUG_CONSERVATIVE with a few extra transforms added
2000+ images AUG_AGGRESSIVE — rotations, affine, color jitter

Nested Transforms

RF-DETR supports OneOf, SomeOf, and Sequential container transforms from Albumentations. The most common pattern is OneOf, which randomly picks one transform from a group:

aug_config = {
    "HorizontalFlip": {"p": 0.5},
    "OneOf": {
        "transforms": [
            {"Rotate": {"limit": 45, "p": 1.0}},
            {"Affine": {"scale": (0.8, 1.2), "p": 1.0}},
        ],
    },
    "GaussianBlur": {"p": 0.2},
}

Each child's p controls its relative selection weight. The container itself always fires.

If you need the same transform twice, or want explicit ordering, pass a list instead of a dict:

aug_config = [
    {"HorizontalFlip": {"p": 0.5}},
    {"Rotate": {"limit": 45, "p": 0.3}},
    {"Rotate": {"limit": 5, "p": 0.5}},  # second Rotate — only possible with list format
]

Bounding boxes are updated automatically when a container holds any geometric transform — no extra configuration needed.

Geometric vs. Pixel-Level Transforms

RF-DETR automatically handles bounding boxes for geometric transforms (flips, rotations, crops, affine, perspective). Pixel-level transforms (blur, noise, color) preserve coordinates unchanged. You don't need to handle this distinction — it's automatic based on the transform name.

Best Practices

Start Conservative

Begin with simple augmentations (horizontal flip, small brightness changes) and gradually add more as needed.

Geometric Transforms

Be careful with aggressive rotations and crops on datasets where object orientation matters (e.g., text detection, oriented objects).

  • Default path: Uses torchvision-native transforms and does not require Albumentations.
  • Custom CPU path: Non-empty aug_config dictionaries use Albumentations and require rfdetr[augment].
  • GPU path: augmentation_backend="kornia" uses Kornia and requires rfdetr[augment].
  • CPU-bound custom configs: More transforms means slower data loading
  • Use num_workers: Parallelize augmentation across data loader workers
  • Monitor training mAP vs validation mAP: With strong augmentations it's normal for training mAP to be lower — validation uses original images while training uses augmented (harder) ones

Troubleshooting

Training is slow — reduce the number of transforms or increase num_workers.

Boxes disappear after augmentation — aggressive rotations or crops can push boxes outside the image boundary. Reduce rotation angles or avoid large crops.

Model not improving — augmentations may be too aggressive. Start with AUG_CONSERVATIVE and add transforms gradually. Try removing geometric transforms first to isolate the cause.

Validation mAP is much higher than training mAP — this is expected with strong augmentations and not a bug. See the monitoring tip above.

Upgrading albumentations to 2.x with existing RandomSizedCrop configs? RF-DETR automatically adapts height/width kwargs to the size=(height, width) format required by albumentations 2.x. No config changes needed.

Advanced: Custom Transforms

Any Albumentations transform works by name. If your custom transform is geometric, register it in rfdetr/datasets/transforms.py so boxes are updated automatically:

GEOMETRIC_TRANSFORMS = {
    ...,
    "YourCustomTransform",
}

Then use it like any other transform:

model.train(
    dataset_dir="...",
    aug_config={
        "HorizontalFlip": {"p": 0.5},
        "YourCustomTransform": {"param": 1, "p": 0.3},
    },
)

Reference

Next Steps