When yolov5 is integrated into Android, the tflite task library cannot be loaded

java.lang.IllegalArgumentException: Error occurred when initializing ImageClassifier: Unexpected number of dimensions for output index 0: got 3D, expected either 2D (BxN with B=1) or 4D (BxHxWxN with B=1, W=1, H=1).

You’re experiencing an issue where the TensorFlow Lite task library on Android is not able to load a YOLOv5 model due to an unexpected output dimension. To resolve this:

  1. Verify Model Output: Ensure the YOLOv5 model’s output dimensions match what the TensorFlow Lite task library expects, typically either 2D or 4D.
  2. Check Model Conversion: During the conversion of YOLOv5 to TFLite, confirm that the output tensors are correctly configured for the expected dimensions.
  3. Use Compatible TFLite Library: Make sure you’re using a TensorFlow Lite task library version that supports the model’s output format. If not, you might need to customize the inference code.
  4. Adjust Model or Inference Code: If necessary, modify the Android inference code to handle the 3D output tensor of YOLOv5.
  5. Inspect TFLite Model: Use tools like Netron to examine the TFLite model and confirm its input and output formats.
  6. Error Handling: Improve error handling in your Android code to get more detailed diagnostics.
  7. Test with Standard Model: Try using a known, working TFLite model to ensure the issue is specific to YOLOv5.
  8. Consult Documentation and Community: Refer to TensorFlow documentation and community forums for additional insights and common solutions.

These steps should help identify and fix the mismatch between the model output and the library’s expectations.