model_yolov8_to_coral

This project aims to provide a Docker container that facilitates the conversion of YOLOv8 models to TensorFlow Lite (TFLite) format. This conversion is crucial for deploying YOLOv8 models on edge devices, such as google-coral ,smartphones, IoT devices, and embedded systems.

Prerequisites

Docker installed on your system.
A trained YOLOv8 model in PyTorch format (.pt).

Installation

1. Clone this repository to your local machine.
2. Place your tflite model in the model_yolo folder at the root of the project.
3. use docker-compose up

Project Structure

    .
    ├── app.py
    ├── docker-compose.yml
    ├── Dockerfile
    ├── model_yolo
    │   ├── yolov8s_672.pt
    │   └── yolov8s_672_saved_model
    │       ├── assets
    │       ├── fingerprint.pb
    │       ├── metadata.yaml
    │       ├── saved_model.pb
    │       ├── variables
    │       │   ├── variables.data-00000-of-00001
    │       │   └── variables.index
    │       ├── yolov8s_672_float16.tflite
    │       ├── yolov8s_672_float32.tflite
    │       ├── yolov8s_672_full_integer_quant_edgetpu.log
    │       ├── yolov8s_672_full_integer_quant_edgetpu.tflite
    │       ├── yolov8s_672_full_integer_quant.tflite
    │       ├── yolov8s_672_int8.tflite
    │       └── yolov8s_672_integer_quant.tflite
    └── README.md