v1.1

README.md

@@ -12,13 +12,13 @@ Download with:
 huggingface-cli download 1x-technologies/worldmodel --repo-type dataset --local-dir data
 ```
 
-Current version: v1.0
+Current version: v1.1
 
-- **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.
+- **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:
+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 log index that frame `i` came from. You may want to use this to separate non-contiguous frames from different videos (transitions).
+- **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):
   ```
   {
@@ -32,4 +32,4 @@ Contents of train/val_v1.0:
 
   
 
-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.
+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.

{train_v1.0 → train_v1.1}/actions/driving_command.bin

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