robro/obstaclenet-voc2007-bbox
obstaclenot-voc2007-grid Binary segmentation masks derived from PASCAL VOC 2007, built for training the seg_net of the ObstacleNet robot-vision pipeline. What is in each row? Each row contains the original JPEG image and a 28×28 binary mask where 255 = obstacle (any object bounding box covers this pixel) and 0 = clear. The mask is 28×28 to match the SegNet output: 32×32 input → two 3×3 convolutions without padding → 28×28 output. Superclass mapping… See the full description on the dataset page: https://huggingface.co/datasets/robro/obstaclenet-voc2007-bbox.
obstaclenot-voc2007-grid
Binary segmentation masks derived from PASCAL VOC 2007, built for training the seg_net of the ObstacleNet robot-vision pipeline.
What is in each row?
Each row contains the original JPEG image and a 28×28 binary mask where 255 = obstacle (any object bounding box covers this pixel) and 0 = clear.
The mask is 28×28 to match the SegNet output: 32×32 input → two 3×3 convolutions without padding → 28×28 output.
Superclass mapping
All 20 VOC classes map to a single binary obstacle channel:
Splits
Usage
from datasets import load_dataset
import numpy as np
ds = load_dataset("YOUR_USERNAME/obstaclenot-voc2007-grid")
row = ds["train"][0]
img = row["image"] # PIL RGB
mask = np.array(row["mask"], dtype=np.float32) / 255. # float32 (28,28) in [0,1]Original dataset — credit and citation
This dataset is a derivative work of PASCAL VOC 2007. All original images remain copyright their respective owners and are subject to the VOC usage guidelines. The segmentation masks added here are released under CC0.
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J. and Zisserman, A. The PASCAL Visual Object Classes (VOC) Challenge. International Journal of Computer Vision, 88(2), 303–338, 2010. <http://host.robots.ox.ac.uk/pascal/VOC/>