Refactor TFLite example. Support FP32, Fp16, INT8 models (#17317)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

examples/YOLOv8-TFLite-Python/README.md

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+# 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:
+
+```bash
+pip install tflite-runtime
+```
+
+### Installing `tensorflow-gpu` (For NVIDIA GPU Users)
+
+Leverage GPU acceleration with NVIDIA GPUs by installing `tensorflow-gpu`:
+
+```bash
+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:
+
+```bash
+pip install tensorflow
+```
+
+## Usage
+
+Follow these instructions to run YOLOv8 after successful installation.
+
+Convert the YOLOv8 model to TFLite format:
+
+```bash
+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:
+
+```bash
+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.
+
+![image](https://github.com/wamiqraza/Attribute-recognition-and-reidentification-Market1501-dataset/blob/main/img/bus.jpg)