Spaces:
Running
Running
File size: 1,603 Bytes
1999a98 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
# 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.
|