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66 lines
1.9 KiB
66 lines
1.9 KiB
11 months ago
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# YOLOv8 - Int8-TFLite Runtime
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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.
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## Installation
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Ensure a smooth setup by following these steps to install necessary dependencies.
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### Installing Required Dependencies
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Install all required dependencies with this simple command:
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```bash
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pip install -r requirements.txt
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```
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### Installing `tflite-runtime`
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To load TFLite models, install the `tflite-runtime` package using:
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```bash
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pip install tflite-runtime
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```
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### Installing `tensorflow-gpu` (For NVIDIA GPU Users)
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Leverage GPU acceleration with NVIDIA GPUs by installing `tensorflow-gpu`:
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```bash
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pip install tensorflow-gpu
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```
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**Note:** Ensure you have compatible GPU drivers installed on your system.
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### Installing `tensorflow` (CPU Version)
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For CPU usage or non-NVIDIA GPUs, install TensorFlow with:
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```bash
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pip install tensorflow
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```
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## Usage
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Follow these instructions to run YOLOv8 after successful installation.
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Convert the YOLOv8 model to Int8 TFLite format:
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```bash
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yolo export model=yolov8n.pt imgsz=640 format=tflite int8
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```
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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:
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```bash
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python main.py --model yolov8n_full_integer_quant.tflite --img image.jpg --conf-thres 0.5 --iou-thres 0.5
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```
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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.
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### Output
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The output is displayed as annotated images, showcasing the model's detection capabilities:
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