dog_detection 0.0.1

Dog face and landmark detection using on-device TFLite models.

dog_detection

[Demo]

On-device dog detection using TFLite models. Detects dogs in images with breed identification, body pose estimation, face localization, and 46-point facial landmarks — all running locally with no remote API.

Features

• Dog body detection with bounding box (SSD-based)
• Breed identification with confidence score
• Body pose estimation via SuperAnimal keypoints
• Face localization and 46-point facial landmark extraction (DogFLW)
• Truly cross-platform: compatible with Android, iOS, macOS, Windows, and Linux
• Background isolate support via DogDetectorIsolate for guaranteed non-blocking UI
• Configurable performance with XNNPACK, GPU, and CoreML acceleration

Quick Start

import 'package:dog_detection/dog_detection.dart';

final detector = DogDetector(mode: DogDetectionMode.full);
await detector.initialize();

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

await detector.dispose();

Dog Face Landmarks (46-Point)

The landmarks property returns a list of 46 DogLandmark objects representing key points on the detected dog face.

Landmark Groups

Group	Count	Points
Left ear	7	Ear outline and tip
Right ear	7	Ear outline and tip
Left eye	4	Eye corners and center
Right eye	4	Eye corners and center
Nose bridge	2	Bridge top and bottom
Nose ring	8	Nostril outline
Mouth/chin	14	Lips, jaw, and chin

Accessing Landmarks

final DogFace face = faces.first;

// 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 dog includes a predicted breed label and confidence score from the species classifier.

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

Bounding Boxes

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

final BoundingBox boundingBox = face.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	16 MB	224×224	Dog face detection and bounding box
Landmark model (full)	55 MB	384×384	46-point facial landmark extraction

Configuration Options

The DogDetector constructor accepts several configuration options:

final detector = DogDetector(
  mode: DogDetectionMode.full,               // Detection mode
  poseModel: AnimalPoseModel.rtmpose,        // Body pose model variant
  landmarkModel: DogLandmarkModel.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	DogDetectionMode	full	Detection mode
poseModel	AnimalPoseModel	rtmpose	Body pose model variant
landmarkModel	DogLandmarkModel	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
faceOnly	Face localizer + face landmarks only (legacy, no SSD)	Fastest

Background Isolate Detection

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

import 'package:dog_detection/dog_detection.dart';

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

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

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

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

When to Use DogDetectorIsolate

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

Performance

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

final detector = DogDetector(
  performanceConfig: PerformanceConfig.xnnpack(),
);
await detector.initialize();

Credits

Models trained on the DogFLW dataset.

Example

The sample code from the pub.dev example tab includes a Flutter app that paints detections onto an image: bounding boxes and 46-point dog facial landmarks.