ArnieRamesh/CounterStrike-1K-360-wds

CounterStrike-1K — 360p WebDataset shards

This repo contains the 360p shards of CounterStrike-1K. Use the main repo to browse the manifest, schema, and subsets.

360p is the recommended resolution for most training pipelines — the actions/state/events/metadata sidecars are identical to the 720p shards, so you can swap resolutions without touching downstream code.

Quickstart

Start a fresh uv project and add the loader:

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

Using pip instead

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

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, sample["state"].shape, len(sample["video"]))

decode_sample(...) returns:

• video: mp4 bytes (H.264 + AAC, 640×360 @ 32 FPS, synchronized stereo audio)
• actions: structured numpy array (per-frame tick, delta_pitch, delta_yaw, 12-button bitmask)
• state: structured numpy array (per-frame view, position, weapon, ammo, HP, money, score, …)
• events: list of sparse round/kill/bomb events
• metadata: public sample metadata sidecar

Size

• 864 shards, ~1.3 TB total
• One round = 10 synchronized POV samples sharing a round_id

Filtering by subset

Most users want a smaller training run. Filter the manifest first, then stream only the matching shards:

from datasets import Video, load_dataset
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",
))
keys = set(manifest[manifest["split"] == "train"]["sample_key"])

shards = load_dataset(
    "ArnieRamesh/CounterStrike-1K-360-wds", split="train", streaming=True,
).cast_column("mp4", Video(decode=False))
for raw in shards:
    if raw["__key__"] in keys:
        sample = decode_sample(raw)
        # ... use sample

License & citation

CC BY-NC 4.0. Citation in the main dataset card.