File size: 1,830 Bytes
c792f5c 94c3645 c792f5c 94c3645 c792f5c 42d40ec c792f5c 94c3645 c792f5c 6e7168e c792f5c 94c3645 6e7168e c792f5c dc29660 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
---
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) (5.5mb)
- [yoloV10s_cs2](https://huggingface.co/Vombit/yolov10s_cs2) (15.7mb)
- [yoloV10m_cs2](https://huggingface.co/Vombit/yolov10m_cs2) (31.9mb)
- [yoloV10b_cs2](https://huggingface.co/Vombit/yolov10b_cs2) (39.7mb)
- [yoloV10l_cs2](https://huggingface.co/Vombit/yolov10l_cs2) (50.0mb)
- [yoloV10x_cs2](https://huggingface.co/Vombit/yolov10x_cs2) (61.4mb)
## 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)
- yolov10l_cs2_fp16.engine (640x640 5 ts, 5 ths, 7.1ms)
- yolov10l_cs2.engine (640x640 5 ts, 5 ths, 16.1ms)
- yolov10l_cs2_fp16.onnx (640x640 5 ts, 5 ths, 337.2ms)
- yolov10l_cs2.onnx (640x640 5 ts, 5 ths, 348.0ms)
- yolov10l_cs2.pt (384x640 5 ts, 5 ths, 99.1ms)
## Dataset info
Data from over 120 games, where the footage has been tagged in detail.
## Train info
The training took place over 150 epochs.
You can also support me with a cup of coffee: [donate](http://185.105.118.103/donation) |