| # 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: | |
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