token_dtype
stringclasses 1
value | s
int64 16
16
| h
int64 16
16
| w
int64 16
16
| vocab_size
int64 262k
262k
| hz
int64 30
30
| tokenizer_ckpt
stringclasses 1
value | num_images
int64 105k
398k
| num_episodes
int64 355
1.24k
| task
stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
data/magvit2.ckpt
| 272,926
| 512
|
pipette
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
data/magvit2.ckpt
| 323,861
| 538
|
pipette
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
data/magvit2.ckpt
| 243,571
| 529
|
pipette
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
data/magvit2.ckpt
| 360,770
| 715
|
pipette
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
data/magvit2.ckpt
| 359,038
| 604
|
pipette
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
data/magvit2.ckpt
| 291,702
| 558
|
pipette
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
data/magvit2.ckpt
| 333,751
| 1,232
|
pipette
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
data/magvit2.ckpt
| 397,589
| 1,237
|
pipette
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
data/magvit2.ckpt
| 325,630
| 1,066
|
pipette
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
data/magvit2.ckpt
| 338,587
| 1,243
|
pipette
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
data/magvit2.ckpt
| 104,622
| 355
|
pipette
|
CyberOrigin Dataset
Our data includes information from home services, the logistics industry, and laboratory scenarios. For more details, please refer to our Offical Data Website
contents of dataset:
cyber_pipette # dataset root path
└── data/
├── metadata_ID1_240808.json
├── segment_ids_ID1_240808.bin # for each frame segment_ids uniquely points to the segment index that frame i came from. You may want to use this to separate non-contiguous frames from different videos (transitions).
├── videos_ID1_240808.bin # 16x16 image patches at 30hz, each patch is vector-quantized into 2^18 possible integer values. These can be decoded into 256x256 RGB images using the provided magvit2.ckpt weights.
├── ...
└── ...
{
"task": "Pipette",
"total_episodes": 8589,
"total_frames": 10384598,
"token_dtype": "uint32",
"vocab_size": 262144,
"fps": 30,
"manipulation_type": "Bi-Manual",
"language_annotation": "None",
"scene_type": "Table Top",
"data_collect_method": "Directly Collection on Human"
}
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