pderrenger
commented
3 days ago @ENNURSILA hello,
Thank you for reaching out and providing detailed information about the issue you're encountering with exporting your YOLOv8 model to TFLite format.
To address your concerns:
Exporting to TFLite
When you export your model using the command:
!yolo export model=/content/runs/detect/train2/weights/best.pt format=tfliteIt generates both best_float16.tflite and best_float32.tflite files. This is expected behavior as the export process creates models in different precision formats to cater to various deployment needs.
Error in TfliteDetector.java
The error you're encountering seems to be related to the integration of the TFLite model within your Android application. To assist you better, could you please provide more details about the specific error message or stack trace you are seeing? Additionally, sharing the relevant parts of your TfliteDetector.java file would be helpful.
Export with half=True and int8=True
Using the command:
!yolo export model=/content/runs/detect/train2/weights/best.pt format=tflite half=True int8=Truegenerates an best_int8.tflite model. The different error you mentioned might be due to the specific requirements or limitations of the int8 quantized model. Int8 models require calibration and might not be directly compatible with all devices or configurations.
General Recommendations
-
Reproducible Example: To help us diagnose the issue more effectively, please provide a minimum reproducible example. This will allow us to understand the context and specifics of the problem. You can find guidelines on how to create one here.
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Latest Versions: Ensure you are using the latest versions of the Ultralytics and TensorFlow Lite packages. This can often resolve compatibility issues.
Example Code for TFLite Integration
Here's a basic example of how you might integrate a TFLite model in an Android application:
try {
// Load the TFLite model from assets
MappedByteBuffer tfliteModel = FileUtil.loadMappedFile(context, "best_float32.tflite");
// Initialize the TFLite interpreter
Interpreter tflite = new Interpreter(tfliteModel);
// Prepare input and output buffers
float[][] input = new float[1][INPUT_SIZE];
float[][] output = new float[1][OUTPUT_SIZE];
// Run inference
tflite.run(input, output);
// Process the output
// ...
} catch (IOException e) {
e.printStackTrace();
}For more detailed instructions on exporting and deploying YOLOv8 models to TFLite, please refer to our documentation.
If you continue to face issues, please provide the additional details mentioned above, and we'll be happy to assist you further.
Hello,
!yolo export model=/content/runs/detect/train2/weights/best.pt format=tflite When I exported my model, it converted it to best_float16.tflite and best_float32.tflite.
I encountered this error.
What parts of the TfliteDetector.java file do I need to change?
my second way
!yolo export model=/content/runs/detect/train2/weights/best.pt format=tflite half=True int8=True best_int8.tflite I encountered a different error. How can I solve it? Thanks a lot.