Bonsai Vineyard (Sample): initial release

.gitattributes

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+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: cc-by-nc-4.0
+pretty_name: "Bonsai Vineyard (Sample)"
+task_categories:
+  - text-to-video
+  - image-to-video
+tags:
+  - agriculture
+  - vineyard
+  - robotics
+  - cosmos
+size_categories:
+  - n<1K
+---
+
+# Bonsai Vineyard (Sample)
+
+Front-facing camera video from a farm robot driving in one vineyard, with per-frame segmentation and depth control
+videos and a text caption for each clip, plus four clips from an orchard and a strawberry field for out-of-domain
+checks. It is the training, validation and test data for the Cosmos3-Nano
+multi-control LoRA recipe in [NVIDIA/cosmos-framework](https://github.com/NVIDIA/cosmos-framework).
+
+## Contents
+
+| Split | Clips | Use |
+|---|---|---|
+| train | 50 | LoRA training |
+| val | 32 | validation loss, checkpoint choice |
+| test | 50 | held-out evaluation, recorded on a different day from train and val |
+| stretch | 4 | out-of-domain check: 2 orchard, 2 strawberry-bed clips (crops not in train) |
+
+Each clip is 150 frames (10 s) at 15 fps, 1280x720, H.264.
+
+```
+clips/<id>/rgb.mp4      camera video
+clips/<id>/seg.mp4      semantic segmentation, colour-coded (legend below)
+clips/<id>/depth.mp4    depth, encoded as below
+clips/<id>/meta.json    frame count, fps, size, depth encoding
+{,val_,test_,stretch_}{vision,seg,depth}.jsonl   cosmos-framework dataset files (train has no prefix);
+                        clip paths are relative to this folder, so point DATASET_PATH here
+metadata.csv            id, split, crop, sequence, motion_class, place, eval_panel, num_frames, fps, caption
+```
+
+- `sequence`: clips cut from the same recording share a sequence id. Clips in one sequence can overlap in time, so
+  keep a sequence within one split if you re-split the data.
+- `crop`: `vineyard`, or `orchard` / `strawberry` for the stretch clips.
+- `motion_class`: `straight`, `turn` or `curve`. `place`: `row` (between vine rows), `edge` (headland) or `yard`.
+- `eval_panel`: the six test clips used for the recipe's before/after evaluation.
+
+### Depth encoding
+
+`depth.mp4` stores inverse depth in 8 bits (grey level `v`): `v = 1 + 254 * (1/z - 1/z_far) / (1/z_near - 1/z_far)`,
+with `z_near = 0.75 m`, `z_far = 15 m`; `v = 0` means no depth. The video is lossy (H.264), so treat recovered metric
+depth as approximate.
+
+### Segmentation
+
+Each pixel of `seg.mp4` is drawn in its class colour. The video is lossy, so map each pixel to the nearest colour in
+the table. The table is shared with Bonsai's other crops; in this dataset almost every pixel is dirt, canopy, sky,
+unassigned, dirt lane, tree trunk (vine trunks and posts), gravel, fence or grass.
+
+| id | colour (RGB) | class |
+|---|---|---|
+| 0 | 0, 0, 0 | unassigned |
+| 5 | 220, 20, 60 | human |
+| 6 | 255, 140, 0 | vehicle |
+| 7 | 128, 64, 128 | building |
+| 8 | 153, 153, 153 | utility_pole |
+| 9 | 0, 60, 100 | water_valve |
+| 10 | 110, 70, 20 | tree_trunk |
+| 11 | 34, 139, 34 | canopy |
+| 12 | 230, 230, 230 | bed (plastic-mulch raised bed) |
+| 13 | 150, 110, 70 | furrow |
+| 14 | 135, 206, 235 | sky |
+| 15 | 255, 105, 180 | mammal |
+| 16 | 255, 182, 193 | bird |
+| 17 | 255, 215, 0 | farm_equipment |
+| 18 | 218, 165, 32 | bee_box |
+| 19 | 250, 170, 30 | sign |
+| 20 | 70, 130, 180 | tank |
+| 21 | 0, 0, 142 | pump |
+| 23 | 190, 153, 153 | fence |
+| 24 | 102, 102, 156 | gate |
+| 25 | 255, 0, 255 | irrigation_line |
+| 26 | 107, 142, 35 | bush |
+| 27 | 0, 255, 127 | crop |
+| 28 | 196, 164, 132 | road (dirt lane) |
+| 29 | 169, 169, 169 | gravel |
+| 30 | 139, 90, 43 | dirt |
+| 31 | 124, 252, 0 | grass |
+| 32 | 101, 67, 33 | mud |
+| 33 | 160, 82, 45 | berm |
+| 34 | 0, 191, 255 | standing_water |
+| 35 | 105, 105, 105 | train_tracks |
+| 36 | 72, 61, 139 | drainage |
+| 37 | 190, 190, 190 | concrete |
+
+## How the data was made
+
+Recorded by Bonsai Robotics farm robots. The vineyard clips come from one vineyard over a few days: train and
+validation clips share recording days, and all test clips come from a separate day. The stretch clips come from an
+orchard and a strawberry field. Clips were chosen to balance straight driving, turns and curves. Segmentation is produced automatically by a
+segmentation model (not hand-labelled), and depth comes from the robot's stereo camera; both are reprojected into the same 1280x720 pinhole
+view as the camera video. For the two strawberry clips the segmentation and depth exist for every 5th frame; the
+frames in between take the nearest labelled frame, shifted to match the camera motion with optical flow. Captions are
+generated from the robot's motion and the sun position at recording time.
+
+## Limitations
+
+- This is a small sample dataset (50 training clips from one vineyard, daylight only; 4 stretch clips). Models trained on it show
+  what a small dataset can do; they will not reach the quality a larger, more varied dataset gives.
+
+## Licence
+
+Released under the [Creative Commons Attribution-NonCommercial 4.0 International licence (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/).
+You may share and adapt the data for non-commercial purposes, with credit to Bonsai Robotics. Commercial use is not
+permitted.
+
+## Citation
+
+```bibtex
+@misc{bonsai_vineyard_sample_2026,
+  title        = {Bonsai Vineyard (Sample)},
+  author       = {{Bonsai Robotics}},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/datasets/BonsaiRobotics/bonsai-vineyard-sample}}
+}
+```

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