| --- |
| license: cc-by-nc-nd-4.0 |
| pipeline_tag: object-detection |
| tags: |
| - yolov10 |
| - ultralytics |
| - yolo |
| - object-detection |
| - pytorch |
| - cs2 |
| - Counter Strike |
| --- |
| |
| Counter Strike 2 players detector |
|
|
| ## Supported Labels |
|
|
| ``` |
| [ 'c', 'ch', 't', 'th' ] |
| ``` |
|
|
| ## All models in this series |
|
|
| - [yoloV10n_cs2](https://huggingface.co/Vombit/yolov10n_cs2) |
| - [yoloV10s_cs2](https://huggingface.co/Vombit/yolov10s_cs2) |
| - [yoloV10m_cs2](https://huggingface.co/Vombit/yolov10m_cs2) |
| - [yoloV10b_cs2](https://huggingface.co/Vombit/yolov10b_cs2) |
| - [yoloV10l_cs2](https://huggingface.co/Vombit/yolov10l_cs2) |
| - [yoloV10x_cs2](https://huggingface.co/Vombit/yolov10x_cs2) |
|
|
| ## How to use |
| ```python |
| # load Yolo |
| from ultralytics import YOLO |
| |
| # Load a pretrained YOLO model |
| model = YOLO(r'weights\yolov**_cs2.pt') |
| |
| # Run inference on 'image.png' with arguments |
| model.predict( |
| 'image.png', |
| save=True, |
| device=0 |
| ) |
| ``` |
|
|
|
|
| ## Predict info |
|
|
| Ultralytics YOLOv8.2.90 🚀 Python-3.12.5 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4060, 8188MiB) |
|
|
| - yolov10x_cs2_fp16.engine (640x640 5 ts, 5 ths, 15.4ms) |
| - yolov10x_cs2.engine (640x640 5 ts, 5 ths, 19.6ms) |
| - yolov10x_cs2_fp16.onnx (640x640 5 ts, 5 ths, 381.7ms) |
| - yolov10x_cs2.onnx (640x640 5 ts, 5 ths, 369.1ms) |
| - yolov10x_cs2.pt (384x640 5 ts, 5 ths, 146.7ms) |
| |
| ## Dataset info |
| |
| Data from over 120 games, where the footage has been tagged in detail. |
| |
|  |
|  |
| |
| |
| ## Train info |
| |
| The training took place over 150 epochs. |
| |
|  |
