File size: 3,379 Bytes
6f7e216 29d0f82 6f7e216 7b13363 6f7e216 356435c 6f7e216 356435c 6f7e216 356435c 6f7e216 356435c 6f7e216 356435c 6f7e216 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | ---
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` |