| ---
|
| license: other
|
| task_categories:
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| - object-detection
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| tags:
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| - counter-strike
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| - cs2
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| - game
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| - yolo
|
| size_categories:
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| - 1K<n<10K
|
| configs:
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| - config_name: default
|
| data_files:
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| - split: train
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| path: data/train/**
|
| - split: validation
|
| path: data/validation/**
|
| dataset_info:
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| features:
|
| - name: image
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| dtype: image
|
| - name: objects
|
| struct:
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| - name: bbox
|
| sequence:
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| sequence: float32
|
| - name: categories
|
| sequence:
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| class_label:
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| names:
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| '0': none
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| '1': ct_body
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| '2': ct_head
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| '3': t_body
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| '4': t_head
|
| ---
|
|
|
| # 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](https://huggingface.co/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.
|
|
|
| Everything that produced this data is open: capture, demo rendering, the labelling
|
| loop and the training and measurement scripts are at
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| [RealTimeObjectDetectionCSGO](https://github.com/fvossel/RealTimeObjectDetectionCSGO).
|
|
|
| ## 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 |
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| | 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](https://www.kaggle.com/datasets/merfarukgnaydn/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.
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| - 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.
|
| |
