Update README.md

README.md

@@ -13,3 +13,23 @@ huggingface-cli download 1x-technologies/worldmodel --repo-type dataset --local-
 ```
 
 Current version: v1.0
+
+- **magvit2.ckpt** - weights for [Magvit2](https://github.com/TencentARC/Open-MAGVIT2) image tokenizer we used. We provide the encoder (tokenizer) and decoder (de-tokenizer) weights.
+
+Contents of train/val_v1.0:
+- **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 log 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_v1.0` 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.