franklinOliveira
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Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> |
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README.md | YOLOv8 INT8 TFLite Inference Example (#7317) | 11 months ago |
main.py | Optimize Example YOLO post-processing speed (#16821) | 2 months ago |
README.md
YOLOv8 - Int8-TFLite Runtime
Welcome to the YOLOv8 Int8 TFLite Runtime for efficient and optimized object detection project. This README provides comprehensive instructions for installing and using our YOLOv8 implementation.
Installation
Ensure a smooth setup by following these steps to install necessary dependencies.
Installing Required Dependencies
Install all required dependencies with this simple command:
pip install -r requirements.txt
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 Int8 TFLite format:
yolo export model=yolov8n.pt imgsz=640 format=tflite int8
Locate the Int8 TFLite model in yolov8n_saved_model
. Choose best_full_integer_quant
or verify quantization at Netron. Then, execute the following in your terminal:
python main.py --model yolov8n_full_integer_quant.tflite --img image.jpg --conf-thres 0.5 --iou-thres 0.5
Replace best_full_integer_quant.tflite
with your model file's path, image.jpg
with your input image, and adjust the confidence (conf-thres) and IoU thresholds (iou-thres) as necessary.
Output
The output is displayed as annotated images, showcasing the model's detection capabilities: