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)	57 MB	256x256	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

For applications that require guaranteed non-blocking UI, use CatDetectorIsolate. This runs the entire detection pipeline in a background isolate, ensuring all processing happens off the main thread.

import 'package:cat_detection/cat_detection.dart';

// Spawn isolate (loads models in background)
final detector = await CatDetectorIsolate.spawn(
  mode: CatDetectionMode.full,
);

// All detection runs in background isolate - UI never blocked
final cats = await detector.detectCats(imageBytes);

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

// Cleanup when done
await detector.dispose();

When to Use CatDetectorIsolate

Use Case	Recommended
Live camera with 60fps UI requirement	CatDetectorIsolate
Processing images in a batch queue	CatDetectorIsolate
Simple single-image detection	CatDetector
Maximum control over pipeline stages	CatDetector

Performance

XNNPACK can be enabled for 2-5x CPU speedup via SIMD vectorization (NEON on ARM, AVX on x86):

final detector = CatDetector(
  performanceConfig: PerformanceConfig.xnnpack(),
);
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.