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README.md

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+---
+license: other
+library_name: ultralytics
+pipeline_tag: object-detection
+tags:
+  - yolo
+  - yolo26
+  - object-detection
+  - counter-strike
+  - cs2
+---
+
+# YOLO26 player detection for CS2 — 640x640 native crops
+
+Detects player bodies and heads, split by team, on a **640x640 centre crop at
+native resolution**. Trained on [fvossel/csgo-object-detection](https://huggingface.co/datasets/fvossel/csgo-object-detection), plus a small set of images that dataset does not redistribute.
+
+> ### ⚠ Intended use
+>
+> This model was trained to study how well a detector performs on a real-time task
+> and what that costs in latency. It is part of a demonstration project, and it is
+> **explicitly not meant to be used for cheating.**
+>
+> Use it offline, against bots, or on your own `-insecure` server. Not on a
+> VAC-secured server, not in matchmaking, not on an account you care about.
+>
+> Pointing a detector at a screen is the easy half. Acting on it is where it falls
+> apart: synthetic mouse input is flagged as injected by the operating system
+> itself, and a system that reacts in milliseconds produces an aim distribution no
+> human produces. Neither is a gap that a more careful implementation closes. The
+> [project repository](https://github.com/fvossel/CSGOAimAssistant) explains this in full and includes the tooling
+> to measure it.
+
+## Metrics
+
+Measured on a **persistent holdout of 1204 images** that no
+training run has ever seen. The split is block-wise by scene, not per frame — a
+random per-frame split puts near-identical neighbouring frames on both sides and
+inflates the numbers.
+
+| | mAP50 | mAP50-95 | Precision | Recall |
+|---|---|---|---|---|
+| overall | 0.905 | 0.727 | 0.937 | 0.840 |
+
+| Class | Instances | Precision | Recall | mAP50 | mAP50-95 |
+|---|---|---|---|---|---|
+| `ct_body` | 563 | 0.922 | 0.867 | 0.919 | 0.797 |
+| `ct_head` | 511 | 0.930 | 0.831 | 0.888 | 0.608 |
+| `t_body` | 677 | 0.930 | 0.833 | 0.908 | 0.784 |
+| `t_head` | 634 | 0.967 | 0.828 | 0.906 | 0.718 |
+
+Class `none` (ID 0) is an empty placeholder kept so the IDs stay stable.
+
+Inference 3.9 ms per image at the reported batch size,
+on the training machine.
+
+## Usage
+
+```python
+from ultralytics import YOLO
+
+model = YOLO("yolo26n_csgo_20260727-231745.pt")
+results = model.predict("crop.png", conf=0.25, iou=0.5)
+```
+
+**Feed it a 640x640 centre crop cut at native resolution, not a resized
+screenshot.** A 1920x1080 frame scaled down to 640 shrinks a head from roughly
+13x17 px to 4x6 px, which is not the scale this model was trained on. This is the
+single most common way to get bad results out of it.
+
+## Training
+
+| | |
+|---|---|
+| Base weight | `yolo26n.pt` |
+| Epochs | 150 |
+| Batch | 32 |
+| Image size | 640 |
+| Optimizer | auto, cosine LR |
+| Mosaic | 1.0, closed for the last 15 epochs |
+
+Augmentation is deliberately conservative: no rotation, no vertical flip, limited
+hue and scale. The game renders a fixed, upright world — augmenting it into poses
+that cannot occur costs capacity without buying robustness.
+
+Only images **confirmed by hand** were trained on. The labelling loop pre-annotates
+with a larger model and then confirms or corrects each image; unchecked model
+output never reaches training.
+
+## Files
+
+* `yolo26n_csgo_20260727-231745.pt` — PyTorch weight, the one to use
+* `yolo26n_csgo_20260727-231745.onnx` — portable ONNX export
+
+No TensorRT engine is published. An `.engine` is tied to the exact GPU, driver and
+TensorRT version it was built on. Build your own:
+
+```bash
+python scripts/export.py --weights yolo26n_csgo_20260727-231745.pt --format engine
+```
+
+## Licence
+
+The training images show Counter-Strike 2 and are derivative of Valve's assets.
+The weights are published for research. Check whether your intended use is covered
+before building on this.

yolo26n_csgo_20260727-231745.onnx

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