Datasets:
File size: 2,547 Bytes
a982e42 e58691d 4bda098 1ba242a 4bda098 495856b 4bda098 495856b 4bda098 495856b eb706c2 b99cefd eb706c2 b99cefd eb706c2 b99cefd 1ba242a a6a6343 1ba242a a6a6343 1ba242a a6a6343 e58691d 773b21c e58691d 4bda098 eb706c2 1ba242a e58691d 8392528 e6d825c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
---
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: 20624104160.0
num_examples: 40000
- name: test
num_bytes: 5112305610.0
num_examples: 10000
download_size: 25802886510
dataset_size: 25736409770.0
- config_name: default-tiny
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 5141667600.0
num_examples: 10000
- name: test
num_bytes: 1287848481.0
num_examples: 2500
download_size: 6434219116
dataset_size: 6429516081.0
- 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
```sh
pip install datasets
```
## Download the dataset
```python
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:
```python
dataset["train"][1]["image"] # PIL image to rendered image
dataset["train"][1]["label"] # PIL image to mask
```
|