Datasets:

TESS-Computer
/

csgo-vla-stage1-16hz

license: mit
task_categories:
  - robotics
  - image-to-text
tags:
  - VLA
  - gaming
  - counter-strike
  - behavioral-cloning
  - imitation-learning
size_categories:
  - 1M<n<10M

CS:GO VLA Stage 1 Dataset (16Hz)

Vision-Language-Action dataset for Counter-Strike: Global Offensive, converted from the TeaPearce CS:GO dataset.

Overview

Frame rate: 16Hz (native, 1 action per frame)
Total samples: ~5.5M frames
Split: train (~~5M) / test (~~500K) following Diamond split
Map: Dust2 deathmatch

Action Format

<|action_start|> mouse_x mouse_y [keys] <|action_end|>

Examples:

<|action_start|> 0 0 <|action_end|>                    # idle
<|action_start|> 5 0 W <|action_end|>                  # walking forward
<|action_start|> -200 50 W A L <|action_end|>          # strafing + shooting

Schema

Column	Type	Description
`id`	string	Unique sample ID
`episode_id`	string	Source HDF5 file
`frame_idx`	int32	Frame number (0-999)
`action`	string	Text-formatted action
`kill_flag`	int32	1 if player got a kill
`death_flag`	int32	1 if player died
`split`	string	"train" or "test"
`image_bytes`	bytes	JPEG screenshot

Usage

from datasets import load_dataset

# Load full dataset
ds = load_dataset("TESS-Computer/csgo-vla-stage1-16hz")

# Filter by split
train_ds = ds.filter(lambda x: x['split'] == 'train')
test_ds = ds.filter(lambda x: x['split'] == 'test')

5Hz chunked variant - 3 actions per sample
Diamond World Model - For evaluation
Original Dataset

CS:GO VLA Stage 1 Dataset (16Hz)

Overview

Action Format

Schema

Usage

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