Game automization
Collection
Automize games with yolov26 • 2 items • Updated
Datasets:
image imagewidth (px) 640 640 | objects dict | source_session stringclasses 8
values |
|---|---|---|
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2
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
289.5,
315.36,
54.69,
161.46
],
[
307.35,
320.85,
18.52,
25.61
]
],
"categories": [
3,
4
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
308.27,
275.74,
28.6,
41.17
],
[
299.76,
276.44,
74.73,
116.92
]
],
"categories": [
4,
3
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
295.99,
283.84,
46.99,
120.41
],
[
308.61,
283.69,
14.73,
22.54
]
],
"categories": [
3,
4
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
192.72,
292.33,
123.09,
330.73
],
[
227.13,
287.67,
30.36,
49.93
]
],
"categories": [
3,
4
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
348.79,
273.58,
70.57,
84.73
],
[
247.37,
270.04,
304.14,
369.96
]
],
"categories": [
4,
3
]
} | 20260724-153124-024a7c | |
{
"bbox": [],
"categories": []
} | 20260724-153124-024a7c | |
{
"bbox": [
[
169.24,
284.37,
122.02,
335.98
],
[
215.81,
281.48,
34,
55.12
]
],
"categories": [
3,
4
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
287.16,
306.53,
51.83,
164
],
[
314.88,
304.46,
16.32,
22.31
]
],
"categories": [
3,
4
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
243.09,
301.62,
66.47,
195.89
],
[
261.92,
300.69,
20.59,
29.04
],
[
243.45,
301.09,
66.82,
195.7
]
],
"categories": [
3,
4,
3
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
311.18,
293.21,
54,
130.29
],
[
328.92,
293.07,
13.53,
14.27
]
],
"categories": [
3,
4
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
204.99,
215.01,
119.26,
336.89
],
[
227.47,
207.91,
34.45,
54.39
]
],
"categories": [
3,
4
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
318.44,
287.71,
32.8,
87.57
],
[
328.52,
286.76,
10.63,
10.76
]
],
"categories": [
3,
4
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
326.77,
271.39,
31.98,
98.84
],
[
335.21,
270.84,
9.74,
15.32
]
],
"categories": [
3,
4
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
145.16,
209.19,
29.58,
40.1
],
[
121.51,
208.79,
92.66,
267.13
],
[
120.86,
209.03,
93.32,
266.55
]
],
"categories": [
2,
1,
1
]
} | 20260724-153124-024a7c | |
{
"bbox": [],
"categories": []
} | 20260724-153124-024a7c | |
{
"bbox": [],
"categories": []
} | 20260724-153124-024a7c | |
{
"bbox": [
[
282.8,
295.45,
27.43,
70.7
],
[
292.39,
295.34,
8.66,
12.31
],
[
487.88,
296.5,
44.59,
111.71
],
[
502.43,
300.02,
12.2,
11.26
]
],
"categories": [
3,
4,
1,
... | 20260724-153124-024a7c | |
{
"bbox": [
[
282.69,
310.95,
98.95,
231.46
],
[
318.35,
311.68,
26.69,
20.33
],
[
282.22,
310.79,
99.72,
230.93
]
],
"categories": [
1,
2,
1
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
320.81,
270.23,
8.1,
9.99
],
[
313.59,
266.97,
28.57,
77.37
]
],
"categories": [
2,
1
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
315.57,
315.66,
10.87,
20.95
],
[
318.51,
318.26,
3.74,
3.07
]
],
"categories": [
3,
4
]
} | 20260724-153124-024a7c | |
{
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[
342.11,
302.34,
42.81,
77.31
],
[
353.1,
302.15,
15.7,
21.86
]
],
"categories": [
1,
2
]
} | 20260724-153124-024a7c | |
{
"bbox": [
[
299.27,
305.33,
47.95,
102.19
],
[
315.57,
304.77,
12.47,
19.51
]
],
"categories": [
3,
4
]
} | 20260724-153124-024a7c | |
{
"bbox": [],
"categories": []
} | 20260724-153124-024a7c | |
{
"bbox": [],
"categories": []
} | 20260724-153124-024a7c | |
{
"bbox": [],
"categories": []
} | 20260724-153124-024a7c | |
{
"bbox": [
[
233.6,
215.38,
252.41,
424.62
],
[
275.08,
214.83,
100.97,
154.16
]
],
"categories": [
3,
4
]
} | 20260724-153124-024a7c |
End of preview. Expand in Data Studio
CS2 player detection - native 640x640 centre crops
5473 labelled frames from Counter-Strike 2 with 10643 boxes for player bodies and heads, split by team.
A YOLO26 detector trained on this data is at fvossel/csgo-player-detection. Its training set is not quite identical: it also contains the images from the source dataset named below, which are not redistributed here.
What makes this different
Every image is a 640x640 centre crop at native resolution - never a resized screenshot. A full frame scaled down to 640 shrinks a head to a handful of pixels and changes its apparent size with the capture resolution. Cropping instead keeps a head exactly the pixel size it has on screen, so a model trained here transfers to live inference at the same crop geometry. Mixing this data with whole-screenshot datasets reintroduces precisely that scale mismatch.
Classes
| ID | Name | train | validation | total |
|---|---|---|---|---|
| 0 | none |
0 | 0 | 0 |
| 1 | ct_body |
2110 | 523 | 2633 |
| 2 | ct_head |
1836 | 470 | 2306 |
| 3 | t_body |
2374 | 638 | 3012 |
| 4 | t_head |
2098 | 594 | 2692 |
Class none exists to keep the IDs stable and carries no instances.
Splits
| Split | Images | Boxes | Images without a box |
|---|---|---|---|
| train | 4357 | 8418 | 900 |
| validation | 1116 | 2225 | 215 |
The split is block-wise, not per frame: consecutive frames from one scene stay together on the same side. Shuffling per frame would put near-identical images in both splits and inflate the validation score. Images without a box are kept on purpose as negative examples.
Box format
objects.bbox is COCO-style [x, y, width, height] in absolute pixels within
the 640x640 crop, with objects.categories parallel to it.
Sources
This dataset started from Counter Strike 2 Body and Head Classification
by Kaggle user merfarukgnaydn. Its five-class scheme with the IDs
none / ct_body / ct_head / t_body / t_head is kept unchanged here, so labels stay
compatible with it.
No image from it is redistributed here.
All 5473 images here were collected and labelled for this dataset, from two kinds of material:
- Live captures from offline matches.
- Frames rendered from community demos via CS Demo Manager, at 5 fps from tick ranges around gunfire, from both the T and the CT perspective. X-ray was off, so no player is visible through a wall.
Sessions: 20260724-152753-b7fd0f, 20260724-153124-024a7c, 20260725-124520-f30d8b, 20260725-125247-becb26, 20260725-185119-fc031b, demo_dust2_ct_01, demo_dust2_t_01, demo_dust2_t_02
Labelling
Boxes were produced by a model and then confirmed or corrected by hand, image by image. Only confirmed images are in this dataset; unchecked model output is not.
Licence and derivation
The images show Counter-Strike 2, so they contain Valve's game assets and are derivative of them. They are published for research on object detection. Check for yourself whether your intended use is covered before you build on this. The annotations themselves are free to use.
The Kaggle dataset that inspired this one states no licence, so none of its images are included here.
Acknowledgement
Thanks to merfarukgnaydn, whose dataset supplied the class scheme this one still
uses and was the starting point for the work.
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