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README.md

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+# Human-50e-11n
+
+## Model Overview
+
+**Architecture:** YOLOv11  
+**Training Epochs:** 50  
+**Batch Size:** 32  
+**Optimizer:** auto  
+**Learning Rate:** 0.0005  
+**Data Augmentation Level:** Moderate
+
+## Training Metrics
+
+- **mAP@0.5:** 0.91583
+
+## Class IDs
+
+| Class ID | Class Name |
+|----------|------------|
+| 0 | Person |
+
+
+## Datasets Used
+
+- detect-human-lg2ng_v1
+- human-detection-grmvx_v1
+- human-detection-p8c2v_v1
+- human-pysi7_v3
+- humans-ziarm_v2
+- people-4evn7-fqlf8-d887c_v3
+- people-4evn7_v2
+- person-dataset-kzsop-vemv4-h1uoh-q5vtx_v2
+- tello-olz2y_v5
+
+## Class Image Counts
+
+| Class Name | Image Count |
+|------------|-------------|
+| Person | 10865 |
+
+
+## Description
+
+This model was trained using the YOLOv11 architecture on a custom dataset. The training process involved 50 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()