cat_detection changelog | Flutter package

2.0.0 #

Added CatDetectionMode.faceOnly, matching the mode dog_detection already had. It runs the face localizer on the whole letterboxed image and then the landmark model, skipping SSD body detection, species classification and body pose. Those three stages account for about 23MB of model weights that are never loaded and, measured on a 3264x2448 photo, roughly 16ms per frame (118.1 ms to 101.7 ms).

Running the localizer on the whole image is what it was trained for; full instead runs it inside an SSD body crop. The localizer emits a single box, so faceOnly returns at most one face however many cats are present, and the returned Cat has no species, breed or pose. full is unchanged and remains the default, still returning body box, species, body pose and face landmarks together.

Adding an enum value is breaking for exhaustive switches over CatDetectionMode, which is why it lands in this release.
Removed CatLandmarkModel.ensemble. The mode required two extra models from a GitHub release that was never published, so selecting it always failed with an HTTP 404 and it has never worked. Rebuilding it was not worthwhile: the only 256px and 320px cat models available are EfficientNetV2S, which measured 156 ms and 242 ms per inference against the bundled MobileNetV3Large model's 83 ms. A three-model ensemble with flip TTA would have cost roughly 960 ms/frame and 109 MB of downloads for an accuracy that was never measured in that mixed-backbone configuration. CatDetector.isEnsembleCached() is removed with it. CatLandmarkModel.full is unchanged and remains the default.
Renamed two groups of CatLandmarkType values that were mislabelled. Coordinates are unaffected; only the names change.

rightEyeTop and rightEyeBottom were swapped. Index 36 sits above index 38 in only 2.9% of the 2079 CatFLW images, and catLandmarkFlipIndex pairs 36 with leftEyeBottom and 38 with leftEyeTop. A horizontal flip preserves vertical position, so both the data and the flip table agree the labels were inverted. Index 36 is now rightEyeBottom and index 38 is rightEyeTop.

The chin contour points were grouped wrongly. catLandmarkFlipIndex mirrors 18 with 20 and 19 with 21, making those the left/right pairs, but the names implied the pairs were 18/19 and 20/21. Index 19 is now chinLeft1 and index 20 is chinRight0.

catLandmarkConnections is updated so the right-eye ring spans the same landmark indices as before.
CatDetector 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 CatDetector the single entry point for the package.
Deprecated CatDetectorIsolate. It is now a thin delegate to CatDetector and will be removed in the next major release. Migration is a rename: CatDetectorIsolate.spawn(...) becomes CatDetector(...) plus await initialize(), detectCats becomes detect, and detectCatsFromMat becomes detectFromMat. onDownloadProgress moves from spawn() to initialize().
CatDetector.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.
CatDetector.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 CatDetectorCore.
detThreshold is now honored on the isolate path. The previous CatDetectorIsolate 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 CatDetector with default (accelerated) performance settings instead of CatDetectorIsolate 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 47.7 ms/frame to 15.1 ms, a 3.2x speedup on the shared body pipeline. The full pipeline goes from 234.6 ms/frame to 113.7 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 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 face landmark model with the MobileNetV3Large 384px variant. The package asset drops from 57.3 MB to 11.6 MB (a 45.7 MB reduction) and accuracy improves: NME_IOD 3.51 vs 3.76 measured in image pixel space over the same 311 held-out CatFLW images, with TFLite invoke latency roughly halved (206 ms -> 103 ms, desktop CPU, 4 threads, XNNPACK). The previously bundled model was the first experiment of the training sweep and had been superseded by later runs.
CatLandmarkModel.full now runs at 384px input instead of 256px. The input resolution is declared once as a constant rather than repeated at each call site, since the interpreter accepts a mismatched resize without erroring and then silently returns garbage coordinates.
Bump modelVersion (_packageVersion 1.0.5 -> 1.5.0, _pipelineVersion pipeline_v1 -> pipeline_v2) so downstream caches invalidate detections produced by the previous model. _packageVersion had been stale since 1.0.5 and did not track the four releases in between.

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

1.0.13 #

Update flutter_litert -> 2.5.5

1.0.12 #

Update flutter_litert -> 2.5.4

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 CatDetector.modelVersion and CatDetector.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 cat face detection and 48-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 #

Fix Windows build: rename private header include guard to avoid collision with public header

0.0.4 #

Fix Windows build: export CatDetectionPluginRegisterWithRegistrar symbol

0.0.3 #

Update animal_detection 0.0.3 -> 0.0.4

0.0.2 #

Add Swift Package Manager support.

0.0.1 #

Initial release with cat face detection and landmark prediction pipeline.

cat_detection 2.0.0 cat_detection: ^2.0.0 copied to clipboard

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