README.md

metadata

license: apache-2.0
pretty_name: 1X World Model Challenge Dataset
size_categories:
  - 10M<n<100M
viewer: false

Dataset for the 1X World Model Challenge.

Download with:

huggingface-cli download 1x-technologies/worldmodel --repo-type dataset --local-dir data

Current version: v1.1

• magvit2.ckpt - weights for MAGVIT2 image tokenizer we used. We provide the encoder (tokenizer) and decoder (de-tokenizer) weights.

Contents of train/val_v1.1:

• video.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 magvig2.ckpt weights.
• segment_ids.bin - for each frame segment_ids[i] 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).
• actions/ - a folder of action arrays stored in np.float32 format. For frame i, the corresponding action is given by driving_command[i], joint_pos[i], l_hand_closure[i], and so on. The shapes of the arrays are as follows (N is the number of frames):

  {
    joint_pos: (N, 21)
    driving_command: (N, 2), 
    neck_desired: (N, 1), 
    l_hand_closure: (N, 1), 
    r_hand_closure: (N, 1),
  }
  

We also provide a small val_v1.1 data split containing held-out examples not seen in the training set, in case you want to try evaluating your model on held-out frames.