Update README.md

README.md

@@ -1,3 +1,4 @@
+```markdown
 ---
 license: apache-2.0
 pretty_name: 1X World Model Challenge Dataset
@@ -5,73 +6,83 @@ size_categories:
 - 10M<n<100M
 viewer: false
 ---
-Dataset for the [1X World Model Challenge](https://github.com/1x-technologies/1xgpt).
 
-Download with:
-```
+# 1X World Model Challenge Dataset
+
+This repository hosts the dataset for the [1X World Model Challenge](https://github.com/1x-technologies/1xgpt).
+
+```bash
 huggingface-cli download 1x-technologies/worldmodel --repo-type dataset --local-dir data
 ```
 
-Changes from v1.1:
-- New train and val dataset of 100 hours, replacing the v1.1 datasets
-- Blur applied to faces
-- Shared a new raw video dataset under CC-BY-NC-SA 4.0: https://huggingface.co/datasets/1x-technologies/worldmodel_raw_data
-- Example scripts to decode Cosmos Tokenized bins `cosmos_video_decoder.py` and load in frame data `unpack_data.py`
+## Updates Since v1.1
 
-Contents of train/val_v2.0:
+- **Train/Val v2.0 (~100 hours)**, replacing v1.1
+- **Test v2.0 dataset for the Compression Challenge**
+- **Faces blurred** for privacy
+- **New raw video dataset** (CC-BY-NC-SA 4.0) at [worldmodel_raw_data](https://huggingface.co/datasets/1x-technologies/worldmodel_raw_data)
+- **Example scripts** now split into:
+  - `cosmos_video_decoder.py` — for decoding Cosmos Tokenized bins
+  - `unpack_data_test.py` — for reading the new test set
+  - `unpack_data_train_val.py` — for reading the train/val sets
 
-The training dataset is shareded into 100 independent shards. The definitions are as follows:
+---
 
-- **video_{shard}.bin**: 8x8x8 image patches at 30hz, with 17 frame temporal window, encoded using [NVIDIA Cosmos Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer) "Cosmos-Tokenizer-DV8x8x8".
-- **segment_idx_{shard}.bin** - Maps each frame `i` to its corresponding segment index. You may want to use this to separate non-contiguous frames from different videos (transitions).
-- **states_{shard}.bin** - States arrays (defined below in `Index-to-State Mapping`) stored in `np.float32` format. For frame `i`, the corresponding state is represented by `states_{shard}[i]`.
-- **metadata** - The `metadata.json` file provides high-level information about the entire dataset, while `metadata_{shard}.json` files contain specific details for each shard. 
+## Train & Val v2.0
 
-  #### Index-to-State Mapping (NEW)
-  ```
-   {
-        0: HIP_YAW
-        1: HIP_ROLL
-        2: HIP_PITCH
-        3: KNEE_PITCH
-        4: ANKLE_ROLL
-        5: ANKLE_PITCH
-        6: LEFT_SHOULDER_PITCH
-        7: LEFT_SHOULDER_ROLL
-        8: LEFT_SHOULDER_YAW
-        9: LEFT_ELBOW_PITCH
-        10: LEFT_ELBOW_YAW
-        11: LEFT_WRIST_PITCH
-        12: LEFT_WRIST_ROLL
-        13: RIGHT_SHOULDER_PITCH
-        14: RIGHT_SHOULDER_ROLL
-        15: RIGHT_SHOULDER_YAW
-        16: RIGHT_ELBOW_PITCH
-        17: RIGHT_ELBOW_YAW
-        18: RIGHT_WRIST_PITCH
-        19: RIGHT_WRIST_ROLL
-        20: NECK_PITCH
-        21: Left hand closure state (0 = open, 1 = closed)
-        22: Right hand closure state (0 = open, 1 = closed)
-        23: Linear Velocity
-        24: Angular Velocity
-    }
+### Format
+
+Each split is sharded:  
+- `video_{shard}.bin` — [NVIDIA Cosmos Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer) discrete DV8×8×8 tokens at 30 Hz  
+- `segment_idx_{shard}.bin` — segment boundaries  
+- `states_{shard}.bin` — `np.float32` states (see below)  
+- `metadata.json` / `metadata_{shard}.json` — overall vs. per‐shard metadata
+
+### State Index Definition (New)
+```
+ 0: HIP_YAW
+ 1: HIP_ROLL
+ 2: HIP_PITCH
+ 3: KNEE_PITCH
+ 4: ANKLE_ROLL
+ 5: ANKLE_PITCH
+ 6: LEFT_SHOULDER_PITCH
+ 7: LEFT_SHOULDER_ROLL
+ 8: LEFT_SHOULDER_YAW
+ 9: LEFT_ELBOW_PITCH
+10: LEFT_ELBOW_YAW
+11: LEFT_WRIST_PITCH
+12: LEFT_WRIST_ROLL
+13: RIGHT_SHOULDER_PITCH
+14: RIGHT_SHOULDER_ROLL
+15: RIGHT_SHOULDER_YAW
+16: RIGHT_ELBOW_PITCH
+17: RIGHT_ELBOW_YAW
+18: RIGHT_WRIST_PITCH
+19: RIGHT_WRIST_ROLL
+20: NECK_PITCH
+21: Left hand closure (0= open, 1= closed)
+22: Right hand closure (0= open, 1= closed)
+23: Linear Velocity
+24: Angular Velocity
+```
+
+---
+
+## Test v2.0
 
+We now provide a **test_v2.0** subset for the Compression World Model Challenge with a similar structure (`video_{shard}.bin`, `states_{shard}.bin`). Use:
+- `unpack_data_test.py` to read the test set
+- `unpack_data_train_val.py` to read train/val
+---
 
-Previous version: v1.1
+## Previous 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.
+- `video.bin` — 16×16 patches at 30Hz, quantized
+- `segment_ids.bin` — segment boundaries
+- `actions/` folder storing multiple `.bin`s for states, closures, etc.
 
-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 `joint_pos[i]`, `driving_command[i]`, `neck_desired[i]`, and so on. The shapes and definitions of the arrays are as follows (N is the number of frames):
-  - **joint_pos** `(N, 21)`: Joint positions. See `Index-to-Joint Mapping` below.  
-  - **driving_command** `(N, 2)`: Linear and angular velocities.
-  - **neck_desired** `(N, 1)`: Desired neck pitch.
-  - **l_hand_closure** `(N, 1)`: Left hand closure state (0 = open, 1 = closed).
-  - **r_hand_closure** `(N, 1)`: Right hand closure state (0 = open, 1 = closed).
-  #### Index-to-Joint Mapping (OLD)
+### v1.1 Joint Index
   ```
    {
         0: HIP_YAW
@@ -97,8 +108,34 @@ Contents of train/val_v1.1:
         20: NECK_PITCH
     }
   
-  ```
+A separate `val_v1.1` set is available.
 
-  
+---
+
+## Provided Checkpoints
+
+- `magvit2.ckpt` from [MAGVIT2](https://github.com/TencentARC/Open-MAGVIT2) used in v1.1
+- For v2.0, see [NVIDIA Cosmos Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer); we supply `cosmos_video_decoder.py`.
+
+---
+
+## Directory Structure Example
+
+```
+train_v1.1/
+val_v1.1/
+train_v2.0/
+val_v2.0/
+test_v2.0/
+  ├── video_{shard}.bin
+  ├── states_{shard}.bin
+  ├── ...
+  ├── metadata_{shard}.json
+cosmos_video_decoder.py
+unpack_data_test.py
+unpack_data_train_val.py
+```
 
-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.
+**License**: [Apache-2.0](./LICENSE)  
+**Author**: 1X Technologies  
+```