CamVid

Attribute

Value

pretty_name

CamVid Dataset

annotations_creators

language_creators

languages

licenses

CC BY-NC-ND 4.0

multilinguality

size_categories

100<n<1K

source_datasets

task_categories

semantic-segmentation

task_ids

paperswithcode_id

camvid

Dataset Description

Dataset Summary

Cambridge-driving Labeled Video Database: Road/driving scene understanding database.

Download and prepare data

Download the data from this github repository and extract it. Replace {PATH_TO_DATA} below with the location of the CamVid folder. Use the following code to load it:

from squirrel_datasets_core.datasets.camvid import CamvidDriver
iter_train = CamvidDriver("{PATH_TO_DATA}").get_iter("train")
iter_val = CamvidDriver("{PATH_TO_DATA}").get_iter("val")
iter_test = CamvidDriver("{PATH_TO_DATA}").get_iter("test")

Dataset Structure

Data Instances

A sample from the training set is provided below:

{
    'image_url': '{PATH_TO_DATA}/train/0016E5_07920.png',
    'label_url': '{PATH_TO_DATA}/trainannot/0016E5_07920.png',
    'split': 'train'
    'image': array(...)
    'label': array(...)
}

Dataset Schema

  • img: A numpy array containing the 480x360 RGB image.

  • label: semantic segmentation map - sky (0), building (1), pole (2), road (3), pavement (4), tree (5), sign/symbol (6), fence (7), car (8), pedestrian (9), bicyclist (10), unlabelled (11)

Data Splits

name

train

val

test

camvid

367

101

233