# Any examples of multiclass pose in yolo-flutter-app?
**URL:** https://community.ultralytics.com/t/any-examples-of-multiclass-pose-in-yolo-flutter-app/1720
**Category:** Discussion
**Tags:** discussion, question, yolo
**Created:** [December 27, 2025, 5:55pm UTC](https://community.ultralytics.com/t/any-examples-of-multiclass-pose-in-yolo-flutter-app/1720 "2025-12-27T17:55:05Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@cyberN8](https://community.ultralytics.com/u/cyberN8)
#### Post date: [December 27, 2025, 5:55pm UTC](https://community.ultralytics.com/t/any-examples-of-multiclass-pose-in-yolo-flutter-app/1720/1 "2025-12-27T17:55:05Z")
Has anyone succeeded in building a Flutter app using either 1) a single YOLO pose model having multiple classes or 2) multiple YOLO pose single-class models on the same camera (video) stream?
I’ve succeeded in transfer learning a single-class yolo11n-pose and exporting a tflite model (via Google Colab) and basing my app on examples [here](https://pub.dev/packages/ultralytics_yolo), but I can’t get any of these methods to succeed for multiclass (3 classes of 6, 4, and 2 keypoints in my case) pose estimation:
1. single model: 3-class, 3d (x, y, visibility) training with rows of annotation files padded to same number (6) of max keypoints (updated YOLO.kt, YOLOView.kt, PoseEstimator.kt files for Android).
2. #1 but 2d (x, y) only.
3. #1 and #2 with annotation files padded to the sum of the total number of keypoints (6+4+2 = 12).
4. Separately trained models for each class with annotations padded to the max (6) number of keypoints (and updated YOLO.kt, YOLOView.kt, PoseEstimator.kt files for Android) and tried to call 3 instances of YOLOView (which probably causes video stream conflicts and/or shows only the last called instance)
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### Author:  [@pderrenger](https://community.ultralytics.com/u/pderrenger)
#### Post date: [December 28, 2025, 1:28pm UTC](https://community.ultralytics.com/t/any-examples-of-multiclass-pose-in-yolo-flutter-app/1720/2 "2025-12-28T13:28:06Z")
Multi-class pose is supported in Ultralytics YOLO (including YOLO11), but the key limitation is that a **single pose model can only have one fixed `kpt_shape` for all classes**. So “3 classes with 6, 4, and 2 keypoints” can’t be represented natively as three different keypoint schemas inside one model head.
The practical way to do this is to **define one global keypoint set** (e.g. `12` keypoints total) and train with `kpt_shape: [12, 3]` (or `[12, 2]`), then for classes that don’t use some keypoints you **pad them as “missing”** (typically `x=0 y=0 v=0` for the unused keypoints). Your app then only renders the subset that applies to the predicted class.
```yaml
# data.yaml
path: /data
train: images/train
val: images/val
names: [c0, c1, c2]
kpt_shape: [12, 3] # fixed for ALL classes
```
If you instead go with multiple single-class models, the main issue in Flutter is exactly what you suspected: **don’t create 3 camera views/streams**. Use **one camera stream** and run **3 interpreters on the same frame buffer** (serially is simplest). With the current `ultralytics_yolo` Flutter examples, that typically means one `YOLOView` and custom native changes if you want multi-model inference in the same pipeline.
One more important gotcha: many mobile examples hardcode COCO assumptions (like 17 keypoints). For custom pose, make sure your Android `PoseEstimator` parsing is derived from the model output shape (compute `nkpt` and `ndim`) rather than hardcoding. A quick sanity check is to confirm the exported model behaves on desktop first by running inference/validation on the exported `.tflite`, since Ultralytics supports predicting directly on exports as shown in the [Pose export docs](https://docs.ultralytics.com/tasks/pose/).
If you paste your `data.yaml` (especially `kpt_shape`) and the TFLite output tensor shape(s) you see on Android, I can tell you exactly what the parser should infer for `nc`, `nkpt`, and keypoint dims for your model.
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### Author:  [@cyberN8](https://community.ultralytics.com/u/cyberN8)
#### Post date: [December 29, 2025, 12:24pm UTC](https://community.ultralytics.com/t/any-examples-of-multiclass-pose-in-yolo-flutter-app/1720/3 "2025-12-29T12:24:17Z")
Thanks, @pderrenger, I’m now exploring OBB. If I can figure out how to query the oriented box coordinates (x, y, angle) from YOLOView/yolo\_result.dart, that will probably meet my needs.