Datasetpublic

ArnieRamesh/CounterStrike-1K-720-wds

CounterStrike-1K — 720p WebDataset shards This repo contains the 720p shards of CounterStrike-1K. Use the main repo to browse the manifest, schema, and subsets. 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… See the full description on the dataset page: https://huggingface.co/datasets/ArnieRamesh/CounterStrike-1K-720-wds.

sourceHugging Facecc-by-nc-4.0updated 5mo agoView on Hugging Face

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Dataset Card

CounterStrike-1K — 720p WebDataset shards

This repo contains the 720p shards of CounterStrike-1K. Use the main repo to browse the manifest, schema, and subsets.

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>

python

from datasets import Video, load_dataset
from counterstrike1k import decode_sample

shards = load_dataset(
    "ArnieRamesh/CounterStrike-1K-720-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, 1280×720 @ 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

—396 shards, ~1.5 TB total
—One round = 10 synchronized POV samples sharing a round_id
—Splits (in manifest.parquet from the main repo): 1,341.7 / 74.5 / 74.5 POV-hours train/val/test

Filtering by subset

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

python

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-720-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.