README.md

metadata

license: mit
size_categories:
  - 10K<n<100K
task_categories:
  - image-segmentation
dataset_info:
  - config_name: default
    features:
      - name: image
        dtype: image
      - name: label
        dtype: image
    splits:
      - name: train
        num_bytes: 20624104160
        num_examples: 40000
      - name: test
        num_bytes: 5112305610
        num_examples: 10000
    download_size: 25802886510
    dataset_size: 25736409770
  - config_name: default-tiny
    features:
      - name: image
        dtype: image
      - name: label
        dtype: image
    splits:
      - name: train
        num_bytes: 5141667600
        num_examples: 10000
      - name: test
        num_bytes: 1287848481
        num_examples: 2500
    download_size: 6434219116
    dataset_size: 6429516081
  - config_name: human-plant
    features:
      - name: image
        dtype: image
      - name: label
        dtype: image
    splits:
      - name: train
        num_bytes: 20529582920
        num_examples: 40000
      - name: test
        num_bytes: 5084631770
        num_examples: 10000
    download_size: 25675082023
    dataset_size: 25614214690
  - config_name: human-plant-tiny
    features:
      - name: image
        dtype: image
      - name: label
        dtype: image
    splits:
      - name: train
        num_bytes: 5117076360
        num_examples: 10000
      - name: test
        num_bytes: 1280707488.5
        num_examples: 2500
    download_size: 6400701649
    dataset_size: 6397783848.5
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
  - config_name: default-tiny
    data_files:
      - split: train
        path: default-tiny/train-*
      - split: test
        path: default-tiny/test-*
  - config_name: human-plant
    data_files:
      - split: train
        path: human-plant/train-*
      - split: test
        path: human-plant/test-*
  - config_name: human-plant-tiny
    data_files:
      - split: train
        path: human-plant-tiny/train-*
      - split: test
        path: human-plant-tiny/test-*

AgroSegNet

This dataset comprises synthetic images captured from a top-down perspective, featuring two distinct annotations: one for direct sunlight and another for human and plant segmentation.

Example loader

Install Hugging Face datasets package

pip install datasets

Download the dataset

from datasets import load_dataset

dataset = load_dataset("Menchen/AgroSegNet","default") # Change "default" to "default-tiny" to preview and test

Load the data

Images and masks are stored as PIL, for example:

dataset["train"][1]["image"] # PIL image to rendered image

dataset["train"][1]["label"] # PIL image to mask