# wheresmycar
First attempt at utilizing YOLOv8 model for vehicle number plate detection.
> [!NOTE]
> This project is for education and skills presentation purposes.
# Motivation
Gain practical knowledge with machine learning technologies in real-world example.
The main goal was to gain hands-on experience in machine learning project utilizing PyTorch library and several technologies to improve software engineering skills.
# About the project
Small Python package providing a class for object detection, utilizing YOLOv8 model which was trained to detect number plates on vehicles.
# Documentation
<a id="plate_detector"></a>
# plate\_detector
plate_detector module
<a id="plate_detector.PlateDetector"></a>
## PlateDetector Objects
```python
class PlateDetector()
```
Class for Vehicle Number Plate Detection based on pretrained YOLOv8 model.
<a id="plate_detector.PlateDetector.load_model"></a>
#### load\_model
```python
def load_model(device: str) -> YOLO
```
Function to load pretrained model.
params:
- device <str>: device on which the model should run
returns:
- <ultralytics.YOLO>: pretrained YOLOv8 model
<a id="plate_detector.PlateDetector.model"></a>
#### model
```python
@property
def model() -> YOLO
```
Access pretrained model
returns:
- <ultralytics.YOLO>: pretrained YOLOv8 model
<a id="plate_detector.PlateDetector.get_device"></a>
#### get\_device
```python
def get_device(enable_cuda: bool) -> str
```
Gets target device for inference.
params:
- enable_cude <bool>: if True, will return cuda if available
returns:
- <str> either cuda or cpu
<a id="plate_detector.PlateDetector.detect"></a>
#### detect
```python
def detect(target_path: str, conf: float = 0.5, **kwargs) -> Results
```
Get predictions on given input.
params:
- target_path <str>: path to directory with images or image's file path
- conf <float>: minimum confidence threshold for detection
returns:
- <ultralytics.engine.results.Results>: inference results
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"description": "# wheresmycar\n\nFirst attempt at utilizing YOLOv8 model for vehicle number plate detection.\n\n> [!NOTE]\n> This project is for education and skills presentation purposes.\n\n\n# Motivation\n\nGain practical knowledge with machine learning technologies in real-world example.\n\nThe main goal was to gain hands-on experience in machine learning project utilizing PyTorch library and several technologies to improve software engineering skills.\n\n# About the project\n\nSmall Python package providing a class for object detection, utilizing YOLOv8 model which was trained to detect number plates on vehicles.\n\n# Documentation\n\n<a id=\"plate_detector\"></a>\n\n# plate\\_detector\n\nplate_detector module\n\n<a id=\"plate_detector.PlateDetector\"></a>\n\n## PlateDetector Objects\n\n```python\nclass PlateDetector()\n```\n\nClass for Vehicle Number Plate Detection based on pretrained YOLOv8 model.\n\n<a id=\"plate_detector.PlateDetector.load_model\"></a>\n\n#### load\\_model\n\n```python\ndef load_model(device: str) -> YOLO\n```\n\nFunction to load pretrained model.\nparams:\n- device <str>: device on which the model should run\nreturns:\n- <ultralytics.YOLO>: pretrained YOLOv8 model\n\n<a id=\"plate_detector.PlateDetector.model\"></a>\n\n#### model\n\n```python\n@property\ndef model() -> YOLO\n```\n\nAccess pretrained model\nreturns:\n- <ultralytics.YOLO>: pretrained YOLOv8 model\n\n<a id=\"plate_detector.PlateDetector.get_device\"></a>\n\n#### get\\_device\n\n```python\ndef get_device(enable_cuda: bool) -> str\n```\n\nGets target device for inference.\nparams:\n- enable_cude <bool>: if True, will return cuda if available\nreturns:\n- <str> either cuda or cpu\n\n<a id=\"plate_detector.PlateDetector.detect\"></a>\n\n#### detect\n\n```python\ndef detect(target_path: str, conf: float = 0.5, **kwargs) -> Results\n```\n\nGet predictions on given input.\nparams:\n- target_path <str>: path to directory with images or image's file path\n- conf <float>: minimum confidence threshold for detection\nreturns:\n- <ultralytics.engine.results.Results>: inference results\n\n",
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