id string | episode_id string | chunk_idx int32 | frame_idx int32 | action string | kill_flag int32 | death_flag int32 | split string | image_bytes unknown |
|---|---|---|---|---|---|---|---|---|
hdf5_dm_july2021_1200_0 | hdf5_dm_july2021_1200 | 0 | 0 | <|action_start|> 0 0 ; -2 10 ; 4 10 <|action_end|> | 1 | 0 | test | [
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hdf5_dm_july2021_1200_1 | hdf5_dm_july2021_1200 | 1 | 3 | <|action_start|> 100 4 A S Shift ; 300 -2 A S ; 60 -2 A S <|action_end|> | 0 | 0 | test | [
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hdf5_dm_july2021_1200_2 | hdf5_dm_july2021_1200 | 2 | 6 | <|action_start|> 0 0 A S ; 0 0 A S ; 200 -10 A S <|action_end|> | 0 | 0 | test | [
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hdf5_dm_july2021_1200_3 | hdf5_dm_july2021_1200 | 3 | 9 | <|action_start|> 500 20 A S ; 200 20 S ; 0 0 A <|action_end|> | 0 | 0 | test | [
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hdf5_dm_july2021_1200_4 | hdf5_dm_july2021_1200 | 4 | 12 | <|action_start|> 60 -4 D ; 100 -4 D ; 200 -4 D <|action_end|> | 0 | 0 | test | [
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hdf5_dm_july2021_1200_5 | hdf5_dm_july2021_1200 | 5 | 15 | <|action_start|> 100 -10 W D ; 2 -4 W D ; 20 -4 W D <|action_end|> | 0 | 0 | test | [
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hdf5_dm_july2021_1200_6 | hdf5_dm_july2021_1200 | 6 | 18 | <|action_start|> 20 2 W D ; 0 0 W D ; 0 0 <|action_end|> | 0 | 0 | test | [
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hdf5_dm_july2021_1200_7 | hdf5_dm_july2021_1200 | 7 | 21 | <|action_start|> 20 -10 ; 30 -20 ; 10 -4 S D Shift <|action_end|> | 0 | 0 | test | [
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hdf5_dm_july2021_1200_8 | hdf5_dm_july2021_1200 | 8 | 24 | <|action_start|> 1000 -100 Space 1 ; 0 0 A ; 0 0 A <|action_end|> | 0 | 0 | test | [
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hdf5_dm_july2021_1200_9 | hdf5_dm_july2021_1200 | 9 | 27 | <|action_start|> 200 0 A ; 500 50 W A ; 100 10 W A Shift <|action_end|> | 0 | 0 | test | [
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End of preview. Expand in Data Studio
CS:GO VLA Stage 1 Dataset (5Hz Chunked)
Vision-Language-Action dataset for Counter-Strike: Global Offensive with action chunking, converted from the TeaPearce CS:GO dataset.
Overview
- Frame rate: 5Hz (every 3rd frame)
- Action chunking: 3 actions per sample (~200ms coverage)
- Total samples: ~1.8M chunks
- Split: train / test following Diamond split
- Map: Dust2 deathmatch
Action Format
<|action_start|> m1_x m1_y [keys1] ; m2_x m2_y [keys2] ; m3_x m3_y [keys3] <|action_end|>
Examples:
<|action_start|> 0 0 ; 0 0 ; 0 0 <|action_end|> # idle
<|action_start|> 5 0 W ; 3 0 W ; 4 0 W <|action_end|> # walking
<|action_start|> -200 50 W L ; -50 10 L ; 10 0 W <|action_end|> # flick shot
Each chunk contains the exact mouse delta and keys for that frame - no aggregation.
Schema
| Column | Type | Description |
|---|---|---|
id |
string | Unique sample ID |
episode_id |
string | Source HDF5 file |
chunk_idx |
int32 | Chunk number within episode |
frame_idx |
int32 | Starting frame number |
action |
string | Text-formatted 3-action chunk |
kill_flag |
int32 | 1 if any kill in chunk |
death_flag |
int32 | 1 if any death in chunk |
split |
string | "train" or "test" |
image_bytes |
bytes | JPEG screenshot (first frame) |
Usage
from datasets import load_dataset
# Load full dataset
ds = load_dataset("TESS-Computer/csgo-vla-stage1-5hz")
# Filter by split
train_ds = ds.filter(lambda x: x['split'] == 'train')
test_ds = ds.filter(lambda x: x['split'] == 'test')
Why 5Hz with Chunking?
- VLA inference speed: 62ms (16Hz) is too fast for current VLMs. 200ms (5Hz) is achievable.
- No information loss: Each chunk predicts exactly what the human did for 3 consecutive frames.
- World model sync: Diamond executes
step(a1), step(a2), step(a3)then returns frame to VLA.
Related
- 16Hz variant - 1 action per frame
- Diamond World Model - For evaluation
- Original Dataset
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