Hi,
I am working on deploying pose estimation models on a Genio 700 platform (MDLA 3.0), using TFLite INT8 models compiled with ncc-tflite.
I observed different behavior between YOLOv8 and YOLO11 models when following the same export pipeline.
System
Ubuntu 22.04
Python 3.10.12
Workflow
I export the models directly using Ultralytics:
YOLOv8:
yolo export model=yolov8s-pose.pt format=tflite imgsz=640 opset=17 simplify=True dynamic=False nms=False batch=1 int8=True
YOLO11:
yolo export model=yolo11n-pose.pt format=tflite imgsz=640 opset=17 simplify=True dynamic=False nms=False batch=1 int8=True
Then I compile on the dla using:
ncc-tflite --arch=mdla3.0 <file>.tflite
Results
$ 20240108_Neuron_SDK_v1.2402.01_neuron-6-0-release/host/bin/ncc-tflite --arch=mdla3.0 yolov8s-pose_full_integer_quant.tflite
$ 20240108_Neuron_SDK_v1.2402.01_neuron-6-0-release/host/bin/ncc-tflite --arch=mdla3.0 yolo11n-pose_full_integer_quant.tflite
ERROR: Fail to create tflite context
$ ls *.dla
yolov8s-pose_full_integer_quant.dla
YOLOv8:
TFLite model compiles successfully
DLA file is generated
Model runs correctly on device
YOLO11:
Compilation fails with:
“ERROR: Fail to create tflite context”
Observations
- Both models are exported using the same parameters
- Both are INT8 TFLite models
- The workflow is identical
- YOLOv8 works, YOLO11 does not
Question
What could cause this difference between YOLOv8 and YOLO11 when targeting MDLA?
- Are there known operator differences or constraints between these models that affect MDLA compatibility?
- Is there any recommended way to adapt YOLO11 models to make them compatible with MDLA?
Thanks in advance.