Update README.md

README.md

@@ -6,4 +6,96 @@ pipeline_tag: object-detection
 tags:
 - traffic
 - birdseye
----
+---
+
+# Model Card for TrafficSurveillance
+
+This model card provides documentation for a YOLOv11m-based object detection model designed to identify pedestrians and vehicles in bird's-eye (aerial) images at 640px resolution.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Muhammed Sezer and Şevval Dikkaya
+- **Model type:** Object Detection (YOLOv11m backbone)
+- **License:** MIT
+- **Finetuned from model:** Ultralytics/YOLO11m
+
+### Model Sources
+
+- **Repository:** https://github.com/sezer-muhammed/Teknofest-Ulasimda-Yapay-Zeka-Veri-Seti
+- **Demo:** https://huggingface.co/spaces/sezer-muhammed/Traffic-Object-Detection
+
+## Uses
+
+### Direct Use
+
+This model is intended for use in surveillance, traffic monitoring, smart city applications, and public safety analysis from drone or aerial views.
+
+### Downstream Use
+
+This model can be fine-tuned for:
+- Domain-specific pedestrian tracking
+
+### Out-of-Scope Use
+
+- Real-time ground-based pedestrian detection
+- Medical, military, or privacy-invasive applications without ethical oversight
+
+## Bias, Risks, and Limitations
+
+This model is trained on publicly sourced aerial data and may underperform in different altitudes, lighting conditions, or non-urban settings. It is also limited to detecting only two classes: pedestrian and vehicle.
+
+### Recommendations
+
+- Apply caution in non-aerial or oblique-angle views.
+- Bias due to limited diversity in dataset origin may affect generalization.
+- This model should not be used in high-stakes applications without human validation.
+
+## How to Get Started with the Model
+
+Load the model with Ultralytics framework and inference on a 640x640 aerial image.
+```python
+from ultralytics import YOLO
+model = YOLO('path/to/yolov11m.pt')
+results = model('your_aerial_image.jpg')
+```
+
+## Training Details
+
+### Training Data
+
+- Dataset: Teknofest AI in Transportation Dataset
+- Classes: `0 = vehicle`, `1 = pedestrian`
+- 25,000+ images and 300,000+ labels
+
+### Training Procedure
+
+#### Preprocessing
+
+- Images resized to 640x640
+- Data split into `train/`, `test/` and `dataset.yaml`
+
+#### Training Hyperparameters
+
+- Mixed precision (fp16)
+- Epochs: 110
+- Optimizer: Adam
+
+#### Summary
+
+The model shows promise for aerial pedestrian and vehicle detection. Additional tuning recommended for deployment in non-standard aerial views.
+
+
+## Citation
+
+```bibtex
+@misc{EflatunDataset,
+  author       = {Dikkaya, Şvval Belkıs and Sezer, Muhammed İzzet},
+  title        = {Eflatun Takımı Teknofest Ulaşımda Yapay Zeka Yarışması Veri Seti},
+  year         = {2021},
+  publisher    = {GitHub},
+  journal      = {GitHub repository},
+  howpublished = {\url{https://github.com/sezer-muhammed/Teknofest-Ulasimda-Yapay-Zeka-Veri-Seti}},
+}
+```