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README.md
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@@ -20,7 +20,7 @@ Contents of train/val_v2.0:
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The training dataset is shareded into 100 independent shards. The shapes and definitions of the arrays are as follows (N is the number of frames).
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- **video_{shard}.bin**: 8x8x8 image patches at 30hz, with 17 frame temporal window, encoded using [NVIDIA Cosmos Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer).
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- **segment_indicies** - For video `n` and frame `i`, `segment_idx_n[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).
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- **robot_states** - States arrays defined in `Index-to-State Mapping` stored in `np.float32` format. For video `n` and frame `i`, the corresponding state is given by `states_n[i]`.
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- **metadata** - The `metadata.json` file provides high-level information about the entire dataset, while `metadata_[n].json` files contain specific details for each individual video `n`.
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The training dataset is shareded into 100 independent shards. The shapes and definitions of the arrays are as follows (N is the number of frames).
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- **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".
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- **segment_indicies** - For video `n` and frame `i`, `segment_idx_n[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).
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- **robot_states** - States arrays defined in `Index-to-State Mapping` stored in `np.float32` format. For video `n` and frame `i`, the corresponding state is given by `states_n[i]`.
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- **metadata** - The `metadata.json` file provides high-level information about the entire dataset, while `metadata_[n].json` files contain specific details for each individual video `n`.
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