--- 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 100+ POV video wall — 1,000+ hours of professional play

100+ POV video wall — 1,000+ hours of professional play

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

Synchronized 10-POV rounds with per-frame action overlays

10 synchronized POVs with per-frame action HUD overlays

Seven active-duty maps

_{Ancient · Anubis · Dust2 · Inferno · Mirage · Nuke · Overpass}

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" ```

Using pip instead

```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" ```

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.