ultralytics/examples/YOLOv8-TFLite-Python

YOLOv8 - TFLite Runtime

This example shows how to run inference with YOLOv8 TFLite model. It supports FP32, FP16 and INT8 models.

Installation

Installing tflite-runtime

To load TFLite models, install the tflite-runtime package using:

pip install tflite-runtime

Installing tensorflow-gpu (For NVIDIA GPU Users)

Leverage GPU acceleration with NVIDIA GPUs by installing tensorflow-gpu:

pip install tensorflow-gpu

Note: Ensure you have compatible GPU drivers installed on your system.

Installing tensorflow (CPU Version)

For CPU usage or non-NVIDIA GPUs, install TensorFlow with:

pip install tensorflow

Usage

Follow these instructions to run YOLOv8 after successful installation.

Convert the YOLOv8 model to TFLite format:

yolo export model=yolov8n.pt imgsz=640 format=tflite int8

Locate the TFLite model in yolov8n_saved_model. Then, execute the following in your terminal:

python main.py --model yolov8n_full_integer_quant.tflite --img image.jpg --conf 0.25 --iou 0.45 --metadata "metadata.yaml"

Replace best_full_integer_quant.tflite with the TFLite model path, image.jpg with the input image path, metadata.yaml with the one generated by ultralytics during export, and adjust the confidence (conf) and IoU thresholds (iou) as necessary.

Output

The output would show the detections along with the class labels and confidences of each detected object.