README.md

metadata

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.

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). You may share and adapt the data for non-commercial purposes, with credit to Bonsai Robotics. Commercial use is not permitted.

Citation

@misc{bonsai_vineyard_sample_2026,
  title        = {Bonsai Vineyard (Sample)},
  author       = {{Bonsai Robotics}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/BonsaiRobotics/bonsai-vineyard-sample}}
}