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# car-75e-11n
## Model Overview
**Architecture:** YOLOv11
**Training Epochs:** 75
**Batch Size:** 32
**Optimizer:** auto
**Learning Rate:** 0.0005
**Data Augmentation Level:** Moderate
## Training Metrics
- **[email protected]:** 0.88072
## Class IDs
| Class ID | Class Name |
|----------|------------|
| 0 | Vehicle |
## Datasets Used
- aerial-cars-rqcqh_v2
- bikedetection-7bpwy_v2
- car-detection-pyxz2_v4
- cars-bytt8_v35
- transport-rhkah_v8
- vehiclecount_v4
- vehicles-q0x2v-8kns4_v1
## Class Image Counts
| Class Name | Image Count |
|------------|-------------|
| Vehicle | 15163 |
## Description
This model was trained using the YOLOv11 architecture on a custom dataset. The training process involved 75 epochs with a batch size of 32. The optimizer used was **auto** with an initial learning rate of 0.0005. Data augmentation was set to the **Moderate** level to enhance model robustness.
## Usage
To use this model for inference, follow the instructions below:
```python
from ultralytics import YOLO
# Load the trained model
model = YOLO('best.pt')
# Perform inference on an image
results = model('path_to_image.jpg')
# Display results
results.show()
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