False positives after converting YOLOv8 to .tflite

Support YOLO
2

Hi Drew! :blush:

It’s great to hear you’re finding Ultralytics helpful! Let’s see if we can resolve the issue with your .tflite model.

Firstly, ensure that the model conversion process is correctly configured. When exporting to .tflite, make sure you’re using the correct image size and input normalization that matches the training configuration. Here’s a quick checklist:

  1. Image Size: Ensure the input image size during inference matches the size used during training. You can specify this during export with imgsz.

  2. Normalization: YOLO models typically expect pixel values in the range [0, 1]. If you’re normalizing to [-1, 1], it might cause issues. Try sticking with [0, 1].

  3. Output Parsing: Double-check the parsing logic for the model’s output. Ensure the dimensions and indices align with the expected output format of YOLO models.

  4. Non-Maximum Suppression (NMS): Ensure your NMS implementation is correctly configured to filter out overlapping boxes.

Here’s a refined export example:

from ultralytics import YOLO

model = YOLO("yolov8s")
model.export(format="tflite", imgsz=640)  # Ensure this matches your training image size

For more detailed guidance, you might find the YOLOv8 Export Documentation helpful.

If the issue persists, consider testing the .tflite model in a simple Python environment using TensorFlow Lite to isolate whether the problem is with the model or the integration with React Native.

Feel free to reach out if you have more questions. Best of luck with your project—it’s a fantastic initiative! :rocket: