Upload folder using huggingface_hub (part 11)

README.md

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+---
+license: other
+task_categories:
+  - object-detection
+tags:
+  - counter-strike
+  - cs2
+  - game
+  - yolo
+size_categories:
+  - 1K<n<10K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/train/**
+      - split: validation
+        path: data/validation/**
+dataset_info:
+  features:
+    - name: image
+      dtype: image
+    - name: objects
+      struct:
+        - name: bbox
+          sequence:
+            sequence: float32
+        - name: categories
+          sequence:
+            class_label:
+              names:
+          0: none
+          1: ct_body
+          2: ct_head
+          3: t_body
+          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.
+
+## 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](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.
+- 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.