ultralytics/examples/YOLOv8-TFLite-Python/README.md

# 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)