| | --- |
| | license: cc-by-nc-nd-4.0 |
| | pipeline_tag: object-detection |
| | tags: |
| | - yolo11 |
| | - ultralytics |
| | - yolo |
| | - object-detection |
| | - pytorch |
| | - cs2 |
| | - Counter Strike |
| | --- |
| | |
| | Counter Strike 2 players detector |
| |
|
| | ## Supported Labels |
| |
|
| | ``` |
| | [ 'c', 'ch', 't', 'th' ] |
| | ``` |
| |
|
| | ## All models in this series |
| |
|
| | - [yolo11n_cs2](https://huggingface.co/Vombit/yolo11n_cs2) |
| | - [yolo11s_cs2](https://huggingface.co/Vombit/yolo11s_cs2) |
| | - [yolo11m_cs2](https://huggingface.co/Vombit/yolo11m_cs2) |
| | - [yolo11l_cs2](https://huggingface.co/Vombit/yolo11l_cs2) |
| | - [yolo11x_cs2](https://huggingface.co/Vombit/yolo11x_cs2) |
| |
|
| | ## How to use |
| | ```python |
| | # load Yolo |
| | from ultralytics import YOLO |
| | |
| | # Load a pretrained YOLO model |
| | model = YOLO(r'weights\yolo**_cs2.pt') |
| | |
| | # Run inference on 'image.png' with arguments |
| | model.predict( |
| | 'image.png', |
| | save=True, |
| | device=0 |
| | ) |
| | ``` |
| |
|
| |
|
| | ## Predict info |
| |
|
| | Ultralytics 8.3.68 🚀 Python-3.11.0 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4060, 8187MiB) |
| |
|
| | - yolo11m_cs2_fp16.engine (384x640 5 ts, 5 ths, 4.0ms) |
| | - yolo11m_cs2.engine (384x640 5 ts, 5 ths, 7.0ms) |
| | - yolo11m_cs2_fp16.onnx (640x640 5 ts, 5 ths, 17.2ms) |
| | - yolo11m_cs2.onnx (384x640 5 ts, 5 ths, 118.8ms) |
| | - yolo11m_cs2.pt (384x640 5 ts, 5 ths, 54.6ms) |
| | |
| | ## Dataset info |
| | |
| | Data from over 127 games, where the footage has been tagged in detail. |
| | |
| |  |
| |  |
| | |
| | |
| | ## Train info |
| | |
| | The training took place over 150 epochs. |
| | |
| |  |
