dog_detection changelog | Flutter package

2.0.0 #

Documented DogDetectionMode.faceOnly properly. Behavior is unchanged, but it was described only as "legacy behavior, no SSD". It runs the face localizer on the whole letterboxed image, which is the input the localizer was trained on, and skips the roughly 23MB of body-stage models entirely. The localizer emits a single box, so the mode returns at most one face however many dogs are present, and the returned Dog has no species, breed or pose.
Removed DogLandmarkModel.ensemble. Swapping the bundled 384px model to MobileNetV3Large left the ensemble mixing backbones: the two downloaded members are still EfficientNetV2S, so the published accuracy figure, measured when all three members shared a backbone, no longer described what the mode actually ran. Re-validating it was not worthwhile. The EfficientNetV2S members measured 156 ms and 242 ms per inference against the bundled model's 83 ms, so the mode cost roughly 960 ms/frame and 109 MB of downloads. Unlike the single bundled model it was never re-measured after the swap, so it was returning results of unknown quality. DogDetector.isEnsembleCached() is removed with it. DogLandmarkModel.full is unchanged and remains the default.

The v0.0.1-models GitHub release is retained so existing 1.x installations keep working.
DogDetector now runs the whole pipeline in a background isolate that it owns. initialize() loads the model assets on the main isolate (where rootBundle is available) and transfers them into a worker it spawns, so detection no longer runs on the calling thread. This makes DogDetector the single entry point for the package.
Deprecated DogDetectorIsolate. It is now a thin delegate to DogDetector and will be removed in the next major release. Migration is a rename: DogDetectorIsolate.spawn(...) becomes DogDetector(...) plus await initialize(), detectDogs becomes detect, and detectDogsFromMat becomes detectFromMat. onDownloadProgress moves from spawn() to initialize().
DogDetector.detectFromMat now takes imageWidth and imageHeight as optional named arguments, defaulting to the Mat's own cols and rows. Existing call sites that pass them keep working.
DogDetector.initialize() no longer accepts useIsolateInterpreter, and initializeFromBuffers is no longer part of the public API. The worker isolate owns interpreter creation, so neither had a meaningful effect on the public class. The buffer-based entry point now lives on the internal DogDetectorCore.
detThreshold is now honored on the isolate path. The previous DogDetectorIsolate never forwarded it to the isolate, so a custom threshold was silently ignored and the pipeline ran at the 0.5 default.
The example app now uses DogDetector with default (accelerated) performance settings instead of DogDetectorIsolate with PerformanceConfig.disabled.
Require animal_detection 2.0.0, which replaces its boxed nested input and output tensors with reused flat Float32Lists handed to TFLite as ByteBuffers. Measured on this pipeline over a 3264x2448 photo in profile mode with PerformanceMode.auto, poseOnly drops from 48.2 ms/frame to 15.1 ms, a 3.2x speedup on the shared body pipeline. The full pipeline goes from 452.1 ms/frame to 114.5 ms, though that figure also includes the landmark model swap below rather than the tensor change alone.
Landmark and pose coordinates shift slightly. animal_detection 2.0.0 fixes ImageUtils.cropAndResize describing an integral crop with pre-truncation floats, which placed landmarks about 0.61px right and 0.52px down of ground truth. Measured over the 311-image CatFLW (measured on cat_detection; the same fix applies here) holdout with real localizer boxes, that cost 0.255 NME_IOD, rising to 1.14 at the 95th percentile, with 72% of images improving. _pipelineVersion is bumped to pipeline_v3 accordingly, so downstream caches re-evaluate stored detections.
AnimalPoseModel.hrnet now works. animal_detection was requesting superanimal_hrnet_w32_256_float16.tflite while its release publishes superanimal_hrnet_w32_float16.tflite, so selecting HRNet failed with an HTTP 404 on first use and had never worked.

1.5.0 #

Replace the bundled dog face landmark model with a MobileNetV3Large backbone (128-channel deconv head) trained on DogFLW at the same 384px input. The asset drops from 54.6 MiB to 11.0 MiB (57.3 MB -> 11.6 MB), a 4.9x reduction, and accuracy improves slightly. Evaluated over all 480 DogFLW test images in absolute image-pixel space at the training crop geometry, NME_IOD is 8.04 versus 8.21 for the previous EfficientNetV2S model. Every facial region is equal or better except mouth (+0.10); both ears, both eyes, nose bridge and nostrils improve. TFLite invoke() also measured about 4x faster on desktop CPU with XNNPACK (134 ms vs 546 ms at 4 threads).

The input/output signature (float32 [1, 384, 384, 3] -> float32 [1, 92]) is unchanged, so this is a drop-in replacement requiring no caller changes.

Caveat on the accuracy figures: DogFLW's test split is also used as the validation set during training (early stopping and best-weight restoration monitor it), so both numbers are optimistic in absolute terms. The new model was also given a longer fine-tuning schedule (400 epochs vs 200), so the improvement is not purely architectural.
Bump the pipeline component of DogDetector.modelVersion to pipeline_v2 so downstream caches holding detections produced by the old model re-evaluate.
Declare the bundled models' input resolutions as named constants rather than repeating integer literals at each call site. A mismatch between the literal and the bundled model is not reported as an error by the interpreter, which resizes the input tensor and then emits garbage coordinates, so the two call sites for each model could previously drift apart silently.
DogDetectorIsolate.spawn now defaults performanceConfig to PerformanceMode.auto (Metal on iOS, XNNPACK elsewhere) instead of PerformanceConfig.disabled. The isolate is the documented path for live camera work, so the previous default silently opted the most performance-sensitive callers out of hardware acceleration while the plain DogDetector constructor already defaulted to auto. Measured on the equivalent cat_detection pipeline over a 3264x2448 photo in profile mode, acceleration off ran 1716 ms/frame versus 438 ms/frame with auto, a 3.9x difference. Callers who relied on the old behaviour should pass PerformanceConfig.disabled explicitly.

1.4.0 #

Update animal_detection -> 1.4.0, which replaces its shipped 12,944-line SSD anchor table with runtime generation. Detection output is unchanged: verified against the real model over 9 images at 100 runs each with identical detection counts, bit-identical scores, and a worst-case box coordinate delta of 9.3e-05 px. The shared library drops from 15,222 to 2,355 lines and the compiled binary shrinks by about 32 KB.
Update flutter_litert -> 3.6.0.

1.3.3 #

Update flutter_litert -> 3.5.0

1.3.2 #

Update flutter_litert -> 3.4.1
Update animal_detection -> 1.3.2

1.3.1 #

Update flutter_litert -> 3.3.1

1.3.0 #

Update flutter_litert -> 3.2.0
Require animal_detection 1.3.0

1.2.3 #

Update flutter_litert -> 3.1.1

1.2.2 #

Update flutter_litert -> 3.1.0

1.2.1 #

Update flutter_litert -> 2.8.3

1.2.0 #

Update flutter_litert -> 2.8.0
Complete Swift Package Manager migration: example apps build via SPM without CocoaPods

1.1.1 #

Remove unused Darwin podspecs for Dart-only iOS/macOS plugin registration.
Require animal_detection 1.1.1.

1.1.0 #

Update animal_detection -> 1.1.0
Update flutter_litert -> 2.5.8

1.0.12 #

Update flutter_litert -> 2.5.5

1.0.11 #

Update flutter_litert to 2.5.3

1.0.10 #

Update flutter_litert -> 2.5.2

1.0.9 #

Update flutter_litert -> 2.5.0

1.0.8 #

Update flutter_litert -> 2.4.1

1.0.7 #

Update flutter_litert -> 2.4.0

1.0.6 #

Update flutter_litert -> 2.3.0

1.0.5 #

Add public DogDetector.modelVersion and DogDetector.modelVersionFor(...) APIs for downstream cache invalidation.

1.0.4 #

Update flutter_litert -> 2.2.0

1.0.3 #

Update flutter_litert -> 2.1.0

1.0.2 #

Update flutter_litert to 2.0.13
Update animal_detection to 1.0.2

1.0.1 #

Update flutter_litert -> 2.0.12

1.0.0 #

First stable release. On-device dog face detection and 46-point facial landmark prediction using TensorFlow Lite. Supports Android, iOS, macOS, Windows, and Linux with automatic hardware acceleration.

0.0.10 #

Update documentation

0.0.9 #

Update flutter_litert 2.0.8 -> 2.0.10

0.0.8 #

Enable auto hardware acceleration by default (XNNPACK on all native platforms, Metal GPU on iOS)
Update flutter_litert 2.0.6 -> 2.0.8
Update animal_detection 0.0.5 -> 0.0.6

0.0.7 #

Fix Android hang on sequential detect calls

0.0.6 #

Fix isolate hanging on sequential detect calls

0.0.5 #

Update animal_detection 0.0.3 -> 0.0.4

0.0.4 #

Fix Xcode build warnings by declaring PrivacyInfo.xcprivacy as a resource bundle in iOS and macOS podspecs

0.0.3 #

Refactor to use shared animal_detection utils

0.0.2 #

Added homepage and repository to pubspec.yaml

0.0.1 #

Initial release
Dog face detection with bounding box
46 facial landmark extraction (ears, eyes, nose, mouth/chin)
DogDetector and DogDetectorIsolate APIs
Support for iOS, Android, macOS, Windows, Linux

dog_detection 2.0.0 dog_detection: ^2.0.0 copied to clipboard

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