---
title: tartan-rgb-train-shards-v1
canonical_url: "https://www.modelscope.cn/datasets/jiashuhong/tartan-rgb-train-shards-v1"
md_url: "https://www.modelscope.cn/datasets/jiashuhong/tartan-rgb-train-shards-v1.md"
repository: jiashuhong/tartan-rgb-train-shards-v1
chinese_name: "Tartan训练RGB顺序分片"
last_updated: 2026-10-25
license: cc-by-4.0
storage_size: "1006 GB"
downloads: 51
stars: 0
---

# tartan-rgb-train-shards-v1

> tartan-rgb-train-shards-v1 - jiashuhong 在 ModelScope 开源的数据集。Tartan RGB training shards

jiashuhong/tartan-rgb-train-shards-v1 是 ModelScope 魔搭社区上的数据集，存储大小 1006 GB，采用 cc-by-4.0 许可。

- **Repository**: jiashuhong/tartan-rgb-train-shards-v1
- **License**: cc-by-4.0
- **Storage size**: 1006 GB
- **Downloads**: 51
- **Stars**: 0
- **Last updated**: 2026-10-25

Source: https://www.modelscope.cn/datasets/jiashuhong/tartan-rgb-train-shards-v1

---

# Tartan RGB training shards

This private mirror contains the **exact RGB corpus selected by the existing training schedule**: 1,247 trajectories, 1,751,563 original PNG files, and 11,318 independent windows. It is a selected-camera repack of upstream datasets, not an independent dataset release or the complete collection of every modality/camera.

| Source | Selected camera | Trajectories | RGB frames |
| --- | --- | ---: | ---: |
| TartanAir V1 | left | 369 | 306,637 |
| TartanGround | left front (`lcam_front`) | 878 | 1,444,926 |

Only original RGB and indexing/provenance are included. There are no model checkpoints, frozen features, generated supervision, KITTI images or labels, depth arrays, poses, or camera calibration labels. Historical opaque pose-member hashes in the unchanged schedule identify the original selection; they are not pose values or training inputs.

## Storage and fidelity

- 622 ZIP shards, with a target size of 2 GiB each. Boundaries occur between complete logical training updates (eight original windows, six in the final update).
- The source ZIP member compression streams are copied directly. Decompressed PNG bytes are unchanged: no image decoding, resizing, quantization, or re-encoding is applied when packing.
- Compressed image payload totals 1,079,274,591,904 bytes (about 1.08 decimal TB), plus small ZIP/index metadata. Original PNG bytes total 1,221,737,915,354 bytes.
- Each shard is independently readable without extraction. The server retains a bounded set of staging shards and removes only its own verified-upload staging files; original source archives are retained.
- Every PNG has a SHA256, byte length, original archive SHA256/member name, and exact window/frame identity in `shards/<id>/index.jsonl.gz`. Every uploaded ZIP/index has a size and SHA256 receipt.

## Layout

```text
schedule/windows.original.json       # unchanged original schedule and order
schedule/shard_plan.json             # shard/update/window ranges
sources.json                         # selected archives and source provenance
shards/00000/rgb.zip                 # original PNG bytes, renamed for uniqueness
shards/00000/index.jsonl.gz           # per-PNG checksums and source mapping
receipts/train-00000.json            # uploaded-file size/SHA256 verification
dataset_manifest.json                # final coverage and all shard hashes
download_shards.py
rgb_shard_reader.py
```

`dataset_manifest.json` is marked `complete` only after all shards are uploaded and independently rechecked against repository file sizes/LFS SHA256. During upload, only shards with an `uploaded_verified` receipt are available for use.

## Small-disk use

Authenticate with the ModelScope SDK in the normal credential cache, or set `MODELSCOPE_API_TOKEN` in your process environment. Do not put a token in a script, Git repository, or a command-line argument.

```bash
python download_shards.py --root /data/tartan-rgb --from-update 1 --ahead 1
```

This fetches the shard containing the requested update and one following shard, resumes partial transfers, and checks SHA256 before making each file usable. No extraction is required. Call `Downloader.fetch_for_update(next_update, ahead=1)` from a background CPU worker while training on the current shard. Remove a consumed shard **only after** saving a checkpoint whose exact sampling cursor has passed its `position_end`. The helper never deletes data automatically.

`RGBShardReader.load(row)` returns the original native RGB window in the same order. Connect it only to data reading; retain the existing teacher/student preprocessing, 81-frame encoder packaging, native backend order, logical rank/RNG assignments, loss, optimizer, depth branch, and saved cursor. This repository does not modify a training run or migrate an existing checkpoint automatically.

The original schedule is bound by SHA256:

`fd0365321281f2f2d223ae81c032a53788a1ee3a9d0a088abc91333f75d177ce`

## Upstream attribution and terms

Images remain the work of the original dataset authors and are redistributed under their **Creative Commons Attribution 4.0 International** terms. This repack changes archive layout and adds checksums/indexes; image content is unchanged. Preserve these attributions when copying the shards.

- **TartanAir:** Wenshan Wang et al., *TartanAir: A Dataset to Push the Limits of Visual SLAM*, IROS 2020. [Official dataset and terms](https://theairlab.org/tartanair-dataset/), [paper](https://arxiv.org/abs/2003.14338).
- **TartanGround:** Manthan Patel, Fan Yang, Yuheng Qiu, Cesar Cadena, Sebastian Scherer, Marco Hutter, and Wenshan Wang, *TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation*, 2025. [Official dataset](https://tartanair.org/tartanground.html), [official publisher card/license](https://huggingface.co/datasets/theairlabcmu/TartanGround), [paper](https://arxiv.org/abs/2505.10696).
- [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/).

```bibtex
@inproceedings{wang2020tartanair,
  title={TartanAir: A Dataset to Push the Limits of Visual SLAM},
  author={Wang, Wenshan and Zhu, Delong and Wang, Xiangwei and Hu, Yaoyu and Qiu, Yuheng and Wang, Chen and Hu, Yafei and Kapoor, Ashish and Scherer, Sebastian},
  booktitle={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2020}
}
@article{patel2025tartanground,
  title={TartanGround: A Large-Scale Dataset for Ground Robot Perception and Navigation},
  author={Patel, Manthan and Yang, Fan and Qiu, Yuheng and Cadena, Cesar and Scherer, Sebastian and Hutter, Marco and Wang, Wenshan},
  journal={arXiv preprint arXiv:2505.10696},
  year={2025}
}
```