| | --- |
| | license: apache-2.0 |
| | datasets: |
| | - rwitz/golf-cart-yolo-data |
| | --- |
| | # GolfCart YOLO11s |
| |
|
| | **Model name:** `golfcart-yolo11s` |
| |
|
| |
|
| | A compact YOLO11s detection model fine-tuned to identify golf carts in aerial and ground-level images. Optimized for fast inference on edge devices while maintaining strong precision across varying lighting and backgrounds. |
| |
|
| | --- |
| |
|
| | ## Model description |
| |
|
| | This model is a lightweight YOLO11s variant trained to **detect golf carts** in diverse environments — fairways, parking lots, garages, and drone captures. It balances speed and accuracy for real-time applications like fleet monitoring, course management, safety analytics, and automated observation systems. |
| |
|
| | * **Architecture:** YOLO11s (small) — one-stage single-shot detector optimized for real-time inference. |
| | * **Input size:** variable; recommended 640×640 or 512×512. |
| | * **Output:** detection mask and confidence score for the class `golf_cart`. |
| |
|
| | --- |
| |
|
| | ## Visual examples |
| |
|
| | Below are example inputs and model outputs: |
| |
|
| |  |
| |
|
| |  |
| |
|
| |  |
| |
|
| | --- |
| |
|
| | ## Intended uses |
| |
|
| | **Primary (appropriate):** |
| |
|
| | * Monitoring golf cart activity on courses. |
| | * Fleet usage and movement tracking. |
| | * Safety or proximity alerts on maintenance vehicles. |
| | * Drone-based detection of golf carts in aerial surveys. |
| |
|
| | **Out-of-scope:** |
| |
|
| | * Identifying individuals or non-cart objects. |
| | * Use in safety-critical or privacy-sensitive contexts without human oversight. |
| |
|
| | --- |
| |
|
| | ## Training data |
| |
|
| | * **Total images:** 624 |
| | * **Content:** golf carts and mixed scenes (including negative examples with no carts) |
| | * **Augmentations:** horizontal flip, brightness/contrast variation, mosaic augmentation, scaling, rotation. |
| | * **Annotation style:** detection labels (not bounding boxes) |
| |
|
| | --- |
| |
|
| | ## Evaluation |
| |
|
| | * **Validation/Evaluation set:** 68 images (balanced with and without golf carts) |
| | * **Results:** |
| |
|
| | * **mAP@50:** 0.93 |
| | * **mAP@50–95:** 0.649 |
| | * **Precision:** 0.978 |
| | * **Recall:** 0.853 |
| |
|
| | These results indicate strong performance even with a relatively small dataset, especially in high-confidence detection of golf carts across mixed environments. |
| |
|
| | --- |
| |
|
| | ## How to use |
| |
|
| | **Install:** |
| |
|
| | ```bash |
| | pip install ultralytics |
| | ``` |
| |
|
| | **Inference example:** |
| |
|
| | ```python |
| | from ultralytics import YOLO |
| | |
| | model = YOLO('best-3.pt') |
| | results = model('test_images/golfcart_drone_01.jpg', imgsz=640) |
| | |
| | results[0].show() # or results[0].save() |
| | ``` |
| |
|
| | Supports ONNX, TorchScript, and TensorRT conversion for deployment. |
| |
|
| | --- |
| |
|
| | ## Limitations and failure modes |
| |
|
| | * Misses can occur when golf carts are small, heavily occluded, or extremely far away in aerial imagery. |
| | * Performance may vary for uncommon cart shapes or regions not represented in the training set. |
| |
|
| | --- |
| |
|
| | ## Biases |
| |
|
| | The dataset contains mostly North American golf cart models and course designs. Accuracy may dip in unfamiliar regional styles or environments. |
| |
|
| | --- |
| |
|
| | ## Ethical considerations |
| |
|
| | * Avoid using this model for surveillance involving people. |
| | * Respect local drone and privacy regulations. |
| |
|
| | --- |
| |
|
| | ## Model parameters |
| |
|
| | * **Weights:** `best-3.pt` |
| | * **Training date:** 2025-10-03 (replace with actual) |
| | --- |
| |
|
| | ## How to cite |
| |
|
| | If you use this model, please cite with the model name and link to its Hugging Face card. |
| |
|
| | --- |
| |
|
| | ## Contact |
| |
|
| | Maintainer: Ryan Witzman — `ryan@rwitz.com` |
