README.md

---
license: cc-by-nc-4.0
pretty_name: CounterStrike-1K
task_categories:
- video-classification
- reinforcement-learning
- video-to-video
- robotics
tags:
- counter-strike-2
- game
- world-model
- video-prediction
- action-conditioned-video
- multi-view
- multi-agent
- webdataset
- audio
- esports
size_categories:
- 10K<n<100K
configs:
- config_name: manifest
  data_files:
  - split: metadata
    path: manifest.parquet
- config_name: rounds
  data_files:
  - split: metadata
    path: round_index.parquet
- config_name: matches
  data_files:
  - split: metadata
    path: match_index.parquet
- config_name: subsets
  data_files:
  - split: train_10h
    path: subsets/train_10h.parquet
  - split: train_50h
    path: subsets/train_50h.parquet
  - split: train_100h
    path: subsets/train_100h.parquet
  - split: train_500h
    path: subsets/train_500h.parquet
  - split: train_1000h
    path: subsets/train_1000h.parquet
  - split: train_all
    path: subsets/train_all.parquet
  - split: dust2_100h
    path: subsets/dust2_100h.parquet
  - split: full_demo_eval
    path: subsets/full_demo_eval.parquet
---

# CounterStrike-1K

<p align="center">
  <img src="media/introduction_video_wall_1000_hours.gif" alt="100+ POV video wall — 1,000+ hours of professional play" width="100%"/>
</p>

**1,490 rendered POV-hours · 7,347 synchronized rounds · 73,470 POV clips · 7 maps · 720p + audio**

<h3 align="center">Synchronized 10-POV rounds with per-frame action overlays</h3>
<p align="center">
  <img src="media/multi_pov_with_actions.gif" alt="10 synchronized POVs with per-frame action HUD overlays" width="100%"/>
</p>

<h3 align="center">Seven active-duty maps</h3>
<p align="center">
  <img src="media/seven_maps.gif" alt="Ancient · Anubis · Dust2 · Inferno · Mirage · Nuke · Overpass" width="100%"/><br/>
  <sub>Ancient · Anubis · Dust2 · Inferno · Mirage · Nuke · Overpass</sub>
</p>

CounterStrike-1K is the first grounded, professional-grade Counter-Strike 2 dataset with **10 synchronized first-person perspectives per round**, captured from professional match demos. It is designed for video world modeling, action-conditioned video prediction, multi-view consistency research, and audio-conditioned learning.

## Quickstart

Start a fresh `uv` project and add the loader:

```bash
mkdir cs1k-demo && cd cs1k-demo
uv init
uv add datasets "counterstrike1k @ git+https://github.com/AnirudhhRamesh/counterstrike1k"
```

<details>
<summary>Using pip instead</summary>

```bash
mkdir cs1k-demo && cd cs1k-demo
python -m venv .venv && source .venv/bin/activate
pip install datasets "counterstrike1k @ git+https://github.com/AnirudhhRamesh/counterstrike1k"
```
</details>

Stream one sample from the public shards — no download required:

```python
from datasets import Video, load_dataset
from counterstrike1k import decode_sample

shards = load_dataset(
    "ArnieRamesh/CounterStrike-1K-360-wds", split="train", streaming=True,
).cast_column("mp4", Video(decode=False))
sample = decode_sample(next(iter(shards)))

print(sample["actions"].shape)   # (frames,) tick, delta_pitch, delta_yaw, buttons bitmask
print(sample["state"].shape)     # (frames,) pos, view, weapon, ammo, hp, money, score, …
print(len(sample["video"]))      # mp4 bytes with synchronized audio
```

Browse the manifest:

```python
import pandas as pd
from huggingface_hub import hf_hub_download

manifest = pd.read_parquet(hf_hub_download(
    "ArnieRamesh/CounterStrike-1K", "manifest.parquet", repo_type="dataset",
))
mirage_train = manifest[(manifest["map_slug"] == "mirage") & (manifest["split"] == "train")]
```

Tiny offline preview (one match, ~2 GB):

```python
from counterstrike1k import load_sample
for sample in load_sample():
    print(sample["metadata"]["sample_key"])
    break
```

Verify that decoded actions actually align with the video — `overlay_frame` draws a HUD with WASD/FIRE/JUMP, mouse delta, HP/armor/money, and score onto any frame:

```python
from counterstrike1k import overlay_frame, overlay_video

overlay_frame(sample, 60)                          # PIL.Image, ready for display()
overlay_video(sample, "debug.mp4", max_frames=192) # full debug clip
```

The end-to-end Jupyter walkthrough is `cs2_release/quickstart.ipynb` in the [source repo](https://github.com/AnirudhhRamesh/CounterStrike-1K).

## Repos

| Repo | Contents | Size |
|---|---|---:|
| `ArnieRamesh/CounterStrike-1K` | Manifest, round/match indices, schema, subsets, Croissant. | ~700 MB |
| `ArnieRamesh/CounterStrike-1K-sample` | One match-map (16 rounds, 160 POV clips). | ~2 GB |
| `ArnieRamesh/CounterStrike-1K-360-wds` | 360p WebDataset shards (recommended for training). | ~1.3 TB |
| `ArnieRamesh/CounterStrike-1K-720-wds` | 720p WebDataset shards. | ~1.5 TB |

## What's in a sample

Each WebDataset sample is one player POV across one round.

| Member | Format | Description |
|---|---|---|
| `mp4` | H.264 + AAC | 720p or 360p video at 32 FPS with synchronized stereo game audio |
| `actions.bin` | packed binary, 14 B/frame | `tick`, `delta_pitch`, `delta_yaw`, 12-button bitmask |
| `state.bin` | packed binary, 37 B/frame | view, world position, active weapon, ammo, HP, armor, money, score, helmet/defuser/bomb |
| `events.json` | JSON | Sparse events: round boundaries, kills (with attacker/victim/assister `pov_idx`), bomb plant/defuse/explode, blinds |
| `json` | JSON | Public sample metadata: ids, alignment, alive window, weapon flags, kill counts |

Buttons (bit order): `FORWARD, BACK, LEFT, RIGHT, JUMP, DUCK, WALK, FIRE, RIGHTCLICK, RELOAD, INSPECT, USE`.

Group POVs of one synchronized round via the shared `round_id` = `match_{12hex}__r{round:03d}`. Sample keys are `match_{12hex}__r{round:03d}__p{pov:02d}` with `pov_idx ∈ {0..9}`.

Full field-level schema is in `schema/`.

## Splits and subsets

Splits are disjoint at the **match-map** level — the same match never appears in two splits.

| Split | POV-hours | Match-maps | Rounds | POV clips |
|---|---:|---:|---:|---:|
| train | 1,341.7 | 301 | 6,573 | 65,730 |
| val   |    74.5 |  21 |   383 |  3,830 |
| test  |    74.5 |  20 |   391 |  3,910 |

Bandwidth-friendly subsets:

```python
ten_hours = pd.read_parquet(hf_hub_download(
    "ArnieRamesh/CounterStrike-1K", "subsets/train_10h.parquet", repo_type="dataset",
))
dust2 = pd.read_parquet(hf_hub_download(
    "ArnieRamesh/CounterStrike-1K", "subsets/dust2_100h.parquet", repo_type="dataset",
))
```

Available: `train_10h.parquet`, `train_50h.parquet`, `train_100h.parquet`, `train_500h.parquet`, `train_1000h.parquet`, `train_all.parquet`, `dust2_100h.parquet`, `full_demo_eval.parquet`.

## Maps

Ancient · Anubis · Dust2 · Inferno · Mirage · Nuke · Overpass — all 7 active-duty competitive maps, balanced by rendered POV-frame count.

## Intended uses

- Action-conditioned video prediction
- Game world modeling
- Multi-view and multi-agent consistency evaluation
- Audio-conditioned prediction
- State-conditioned modeling
- Representation learning

## Out of scope

- Re-identifying players or linking players across matches
- Recovering Steam IDs or online accounts
- Player profiling, ranking, anti-cheat, surveillance

Public artifacts contain no Steam IDs, online account identifiers, raw HLTV identifiers, profile URLs, player names, or chat text. `pov_idx` is anonymous and only stable within a single match.

## Citation

```bibtex
@dataset{counterstrike1k2026,
  title     = {CounterStrike-1K: Synchronized Multi-POV Counter-Strike 2 for World Modeling},
  author    = {Ramesh, Anirudhh},
  year      = {2026},
  publisher = {Hugging Face},
  version   = {1.0.0},
  url       = {https://huggingface.co/datasets/ArnieRamesh/CounterStrike-1K}
}
```

## License

CounterStrike-1K release artifacts are distributed for non-commercial research under CC BY-NC 4.0 to the extent of the authors' rights. Counter-Strike 2 and underlying game assets remain property of Valve Corporation. Raw HLTV demo files are not redistributed.