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README.md

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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()