Instructions to use Vombit/yolov10b_cs2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- YOLOv10
How to use Vombit/yolov10b_cs2 with YOLOv10:
from ultralytics import YOLOvv10 model = YOLOvv10.from_pretrained("Vombit/yolov10b_cs2") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - ultralytics
How to use Vombit/yolov10b_cs2 with ultralytics:
from ultralytics import YOLOvv10 model = YOLOvv10.from_pretrained("Vombit/yolov10b_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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Ultralytics YOLOv8.2.90 🚀 Python-3.12.5 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4060, 8188MiB)
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- yolov10b_cs2_fp16.engine ()
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- yolov10b_cs2.engine ()
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- yolov10b_cs2_fp16.onnx ()
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- yolov10b_cs2.onnx ()
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- yolov10b_cs2.pt ()
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## Dataset info
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Ultralytics YOLOv8.2.90 🚀 Python-3.12.5 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4060, 8188MiB)
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- yolov10b_cs2_fp16.engine (640x640 5 ts, 5 ths, 7.1ms)
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- yolov10b_cs2.engine (640x640 5 ts, 5 ths, 11.2ms)
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- yolov10b_cs2_fp16.onnx (640x640 5 ts, 5 ths, 246.2ms)
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- yolov10b_cs2.onnx (640x640 5 ts, 5 ths, 257.6ms)
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- yolov10b_cs2.pt (384x640 5 ts, 5 ths, 114.4ms)
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## Dataset info
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