PipelinePresets class abstract

Pre-configured preprocessing pipelines for common ML models.

Use these factory methods to quickly create pipelines that match the preprocessing requirements of popular model architectures.

// Get an ImageNet classification pipeline
final pipeline = PipelinePresets.imagenetClassification();

// Or create a custom pipeline
final custom = PipelinePresets.custom(
  height: 256,
  width: 256,
  mean: [0.5, 0.5, 0.5],
  std: [0.5, 0.5, 0.5],
);

Constructors

PipelinePresets()

Properties

hashCode → int

The hash code for this object.

no setter, inherited

runtimeType → Type

A representation of the runtime type of the object.

no setter, inherited

Methods

noSuchMethod(Invocation invocation) → dynamic

Invoked when a nonexistent method or property is accessed.

inherited

toString() → String

A string representation of this object.

inherited

Operators

operator ==(Object other) → bool

The equality operator.

inherited

Static Methods

clip({int size = 224, InterpolationMode interpolation = InterpolationMode.bicubic}) → TensorPipeline

Creates a pipeline for CLIP vision encoder.

custom({required int height, required int width, InterpolationMode interpolation = InterpolationMode.bilinear, List<double>? mean, List<double>? std, bool addBatchDim = true, bool toChw = true}) → TensorPipeline

Creates a fully customizable preprocessing pipeline.

faceRecognition({int height = 112, int width = 112, InterpolationMode interpolation = InterpolationMode.bilinear}) → TensorPipeline

Creates a pipeline for face recognition models (e.g., ArcFace).

imagenetClassification({int shortestEdge = 256, int cropSize = 224, InterpolationMode interpolation = InterpolationMode.bilinear}) → TensorPipeline

Creates a pipeline for ImageNet classification models.

minimal({int height = 224, int width = 224}) → TensorPipeline

Creates a minimal preprocessing pipeline with just resize and normalize.

mobileNet({int height = 224, int width = 224, InterpolationMode interpolation = InterpolationMode.bilinear}) → TensorPipeline

Creates a pipeline for MobileNet models.

objectDetection({int height = 640, int width = 640, InterpolationMode interpolation = InterpolationMode.bilinear}) → TensorPipeline

Creates a pipeline for object detection models (e.g., YOLO).

resnetClassification({int height = 224, int width = 224, InterpolationMode interpolation = InterpolationMode.bilinear}) → TensorPipeline

Creates a pipeline for ResNet classification models.

segmentation({int height = 512, int width = 512, InterpolationMode interpolation = InterpolationMode.bilinear}) → TensorPipeline

Creates a pipeline for semantic segmentation models.

tflite({int height = 224, int width = 224, bool normalize = true}) → TensorPipeline

Creates a pipeline for TensorFlow Lite models.

vit({int size = 224, InterpolationMode interpolation = InterpolationMode.bilinear}) → TensorPipeline

Creates a pipeline for Vision Transformer (ViT) models.