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--- |
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license: cc-by-nc-nd-4.0 |
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pipeline_tag: object-detection |
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tags: |
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- yolo11 |
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- ultralytics |
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- yolo |
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- object-detection |
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- pytorch |
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- cs2 |
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- Counter Strike |
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--- |
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Counter Strike 2 players detector |
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## Supported Labels |
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``` |
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[ 'c', 'ch', 't', 'th' ] |
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``` |
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## All models in this series |
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- [yolo11n_cs2](https://huggingface.co/Vombit/yolo11n_cs2) (~6mb) |
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- [yolo11s_cs2](https://huggingface.co/Vombit/yolo11s_cs2) (~18mb) |
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- [yolo11m_cs2](https://huggingface.co/Vombit/yolo11m_cs2) (~39mb) |
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- [yolo11l_cs2](https://huggingface.co/Vombit/yolo11l_cs2) (~49mb) |
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- [yolo11x_cs2](https://huggingface.co/Vombit/yolo11x_cs2) (~109mb) |
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## How to use |
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```python |
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# load Yolo |
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from ultralytics import YOLO |
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# Load a pretrained YOLO model |
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model = YOLO(r'weights\yolo**_cs2.pt') |
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# Run inference on 'image.png' with arguments |
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model.predict( |
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'image.png', |
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save=True, |
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device=0 |
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) |
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``` |
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## Predict info |
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Ultralytics 8.3.68 🚀 Python-3.11.0 torch-2.5.1+cu124 CUDA:0 (NVIDIA GeForce RTX 4060, 8187MiB) |
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- yolo11n_cs2_fp16.engine (384x640 5 ts, 5 ths, 20.2ms) |
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- yolo11n_cs2.engine (384x640 5 ts, 5 ths, 3.3ms) |
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- yolo11n_cs2_fp16.onnx (640x640 5 ts, 5 ths, 7.8ms) |
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- yolo11n_cs2.onnx (384x640 5 ts, 5 ths, 172.7ms) |
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- yolo11n_cs2.pt (384x640 5 ts, 5 ths, 52.1ms) |
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## Dataset info |
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Data from over 127 games, where the footage has been tagged in detail. |
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## Train info |
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The training took place over 150 epochs. |
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You can also support me with a cup of coffee: [donate](http://185.105.118.103/donation) |
