You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
65 lines
1.9 KiB
65 lines
1.9 KiB
# 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: |
|
|
|
```bash |
|
pip install -r requirements.txt |
|
``` |
|
|
|
### 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 Int8 TFLite format: |
|
|
|
```bash |
|
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](https://netron.app/). Then, execute the following in your terminal: |
|
|
|
```bash |
|
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: |
|
|
|
|
|
|