cat_detection 2.0.0

Cat face and landmark detection using on-device LiteRT (formerly TensorFlow Lite) models.

cat_detection

On-device cat detection using TFLite models. Detects cats in images with breed identification, body pose estimation, face localization, and 48-point facial landmarks.

Features

• Cat body detection with bounding box (SSD-based)
• Breed identification with confidence score
• Body pose estimation via SuperAnimal keypoints
• Face localization and 48-point facial landmark extraction (CatFLW)
• Truly cross-platform: compatible with Android, iOS, macOS, Windows, and Linux
• Configurable performance with XNNPACK, GPU, and CoreML acceleration

Quick Start

import 'package:cat_detection/cat_detection.dart';

final detector = CatDetector(mode: CatDetectionMode.full);
await detector.initialize();

final cats = await detector.detect(imageBytes);
for (final cat in cats) {
  print('${cat.species} at ${cat.boundingBox}');
  print('Breed: ${cat.breed} (${(cat.speciesConfidence! * 100).toStringAsFixed(0)}%)');
  print('Pose keypoints: ${cat.pose?.landmarks.length}');
  print('Face landmarks: ${cat.face?.landmarks.length}');
}

await detector.dispose();

Cat Face Landmarks (48-Point)

The landmarks property returns a list of 48 CatLandmark objects representing key points on the detected cat face.

Landmark Groups

Group	Count	Points
Left ear	5	Ear contour
Right ear	5	Ear contour
Left eye	7	Eye corners, contour, and center
Right eye	7	Eye corners, contour, and center
Nose bridge	2	Bridge left and right
Nose ring	4	Nostril outline
Nose tips/wings	4	Nose tip and wing points
Mouth/chin	10	Lips, jaw, muzzle, and chin
Face contour	4	Face outline and muzzle center

Accessing Landmarks

final CatFace face = cat.face!;

// Iterate through all landmarks
for (final landmark in face.landmarks) {
  print('${landmark.type.name}: (${landmark.x}, ${landmark.y})');
}

Breed Identification

In full and poseOnly modes, each detected cat includes a predicted breed label and confidence score from the species classifier.

final cats = await detector.detect(imageBytes);
for (final cat in cats) {
  if (cat.breed != null) {
    print('Breed: ${cat.breed}');
    print('Confidence: ${(cat.speciesConfidence! * 100).toStringAsFixed(1)}%');
  }
}

Bounding Boxes

The boundingBox property returns a BoundingBox object representing the cat body bounding box in absolute pixel coordinates.

final BoundingBox boundingBox = cat.boundingBox;

// Access edges
final double left = boundingBox.left;
final double top = boundingBox.top;
final double right = boundingBox.right;
final double bottom = boundingBox.bottom;

// Calculate dimensions
final double width = boundingBox.right - boundingBox.left;
final double height = boundingBox.bottom - boundingBox.top;

print('Box: ($left, $top) to ($right, $bottom)');
print('Size: $width x $height');

Model Details

Model	Size	Input	Purpose
Face localizer	17 MB	224x224	Cat face detection and bounding box
Landmark model (full)	11 MB	384x384	48-point facial landmark extraction

Configuration Options

The CatDetector constructor accepts several configuration options:

final detector = CatDetector(
  mode: CatDetectionMode.full,               // Detection mode
  poseModel: AnimalPoseModel.rtmpose,        // Body pose model variant
  landmarkModel: CatLandmarkModel.full,      // Face landmark model variant
  cropMargin: 0.20,                          // Margin around detected body for crop
  detThreshold: 0.5,                         // SSD detection confidence threshold
  interpreterPoolSize: 1,                    // TFLite interpreter pool size
  performanceConfig: PerformanceConfig.disabled, // Performance optimization
);

Option	Type	Default	Description
mode	CatDetectionMode	full	Detection mode
poseModel	AnimalPoseModel	rtmpose	Body pose model variant
landmarkModel	CatLandmarkModel	full	Face landmark model variant
cropMargin	double	0.20	Margin around detected body crop (0.0-1.0)
detThreshold	double	0.5	SSD detection confidence threshold
interpreterPoolSize	int	1	TFLite interpreter pool size
performanceConfig	PerformanceConfig	disabled	Hardware acceleration config

Detection Modes

Mode	Features	Speed
full	Body detection + breed ID + body pose + face landmarks	Standard
poseOnly	Body detection + breed ID + body pose (no face)	Faster

Background Isolate Detection

Detection always runs in a background isolate. CatDetector spawns and owns that isolate during initialize(), so the whole pipeline (decode, SSD, species, pose, localizer, landmarks) stays off the main thread and the UI is never blocked. There is nothing extra to opt into:

import 'package:cat_detection/cat_detection.dart';

// initialize() loads the models and spawns the worker isolate
final detector = CatDetector(mode: CatDetectionMode.full);
await detector.initialize();

// Runs in the background isolate; the UI thread stays free
final cats = await detector.detect(imageBytes);

for (final cat in cats) {
  print('${cat.breed} at ${cat.boundingBox}');
  print('Face landmarks: ${cat.face?.landmarks.length}');
}

// Tears down the isolate and frees the native interpreters
await detector.dispose();

Model bytes are transferred into the isolate with TransferableTypedData, so the ~70MB of weights in the default configuration move without being copied.

Migrating from CatDetectorIsolate

CatDetectorIsolate is deprecated and will be removed in the next major release. It now just delegates to CatDetector, so migration is a rename:

Before	After
await CatDetectorIsolate.spawn(...)	CatDetector(...) then await initialize()
detector.detectCats(bytes)	detector.detect(bytes)
detector.detectCatsFromMat(mat)	detector.detectFromMat(mat)
detector.dispose()	detector.dispose() (unchanged)

onDownloadProgress moves from spawn() to initialize(); every other configuration argument stays on the CatDetector constructor.

Performance

Hardware Acceleration

The package automatically selects the best acceleration strategy for each platform:

Platform	Default Delegate	Speedup	Notes
macOS	XNNPACK	2-5x	SIMD vectorization (NEON on ARM, AVX on x86)
Linux	XNNPACK	2-5x	SIMD vectorization
iOS	Metal GPU	2-4x	Hardware GPU acceleration
Android	XNNPACK	2-5x	ARM NEON SIMD acceleration
Windows	XNNPACK	2-5x	SIMD vectorization (AVX on x86)

No configuration needed, just call initialize() and you get the optimal performance for your platform.

Advanced Performance Configuration

// Auto mode (default), optimal for each platform
await detector.initialize();

// Force XNNPACK (all native platforms)
final detector = CatDetector(
  performanceConfig: PerformanceConfig.xnnpack(numThreads: 4),
);
await detector.initialize();

// Force GPU delegate (iOS recommended, Android experimental)
final detector = CatDetector(
  performanceConfig: PerformanceConfig.gpu(),
);
await detector.initialize();

// CPU-only (maximum compatibility)
final detector = CatDetector(
  performanceConfig: PerformanceConfig.disabled,
);
await detector.initialize();

Credits

Models trained on the CatFLW dataset.

Example

The sample code from the pub.dev example tab includes a Flutter app that paints detections onto an image: bounding boxes and 48-point cat facial landmarks.