Instructions to use Vombit/yolov10n_cs2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- YOLOv10
How to use Vombit/yolov10n_cs2 with YOLOv10:
from ultralytics import YOLOvv10 model = YOLOvv10.from_pretrained("Vombit/yolov10n_cs2") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - ultralytics
How to use Vombit/yolov10n_cs2 with ultralytics:
from ultralytics import YOLOvv10 model = YOLOvv10.from_pretrained("Vombit/yolov10n_cs2") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
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README.md
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## All models in this series
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## How to use
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```python
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Data from over 100 games, where the footage has been tagged in detail.
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## Train info
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The training took place over
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## All models in this series
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- [yoloV10n_cs2](https://huggingface.co/Vombit/yolov10n_cs2) (5.5mb)
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## How to use
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```python
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Data from over 100 games, where the footage has been tagged in detail.
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<img width="640" src="https://huggingface.co/Vombit/yolov10n_cs2/resolve/main/labels.jpg">
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<img width="640" src="https://huggingface.co/Vombit/yolov10n_cs2/resolve/main/labels_correlogram.jpg">
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## Train info
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The training took place over 150 epochs.
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<img width="640" src="https://huggingface.co/Vombit/yolov9c_cs2/resolve/main/results.png">
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