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 7.64k
504k
|
|---|---|---|---|---|---|---|---|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
imagenet_256_L.ckpt
| 451,010
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
imagenet_256_L.ckpt
| 142,409
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
imagenet_256_L.ckpt
| 504,486
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
imagenet_256_L.ckpt
| 502,831
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
imagenet_256_L.ckpt
| 462,158
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
imagenet_256_L.ckpt
| 406,734
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
imagenet_256_L.ckpt
| 7,638
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
imagenet_256_L.ckpt
| 171,681
|
uint32
| 16
| 16
| 16
| 262,144
| 30
|
imagenet_256_L.ckpt
| 302,218
|
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 the dataset:
cyber_fold_towels # 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": "Fold Towels",
"total_episodes": 6927,
"total_frames": 2951165,
"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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