albertvillanova HF staff commited on
Commit
b21d907
1 Parent(s): d458b71

Delete loading script

Browse files
Files changed (1) hide show
  1. beans.py +0 -102
beans.py DELETED
@@ -1,102 +0,0 @@
1
- # coding=utf-8
2
- # Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor.
3
- #
4
- # Licensed under the Apache License, Version 2.0 (the "License");
5
- # you may not use this file except in compliance with the License.
6
- # You may obtain a copy of the License at
7
- #
8
- # http://www.apache.org/licenses/LICENSE-2.0
9
- #
10
- # Unless required by applicable law or agreed to in writing, software
11
- # distributed under the License is distributed on an "AS IS" BASIS,
12
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
- # See the License for the specific language governing permissions and
14
- # limitations under the License.
15
- """Beans leaf dataset with images of diseased and health leaves."""
16
-
17
- import os
18
-
19
- import datasets
20
- from datasets.tasks import ImageClassification
21
-
22
-
23
- _HOMEPAGE = "https://github.com/AI-Lab-Makerere/ibean/"
24
-
25
- _CITATION = """\
26
- @ONLINE {beansdata,
27
- author="Makerere AI Lab",
28
- title="Bean disease dataset",
29
- month="January",
30
- year="2020",
31
- url="https://github.com/AI-Lab-Makerere/ibean/"
32
- }
33
- """
34
-
35
- _DESCRIPTION = """\
36
- Beans is a dataset of images of beans taken in the field using smartphone
37
- cameras. It consists of 3 classes: 2 disease classes and the healthy class.
38
- Diseases depicted include Angular Leaf Spot and Bean Rust. Data was annotated
39
- by experts from the National Crops Resources Research Institute (NaCRRI) in
40
- Uganda and collected by the Makerere AI research lab.
41
- """
42
-
43
- _URLS = {
44
- "train": "https://huggingface.co/datasets/beans/resolve/main/data/train.zip",
45
- "validation": "https://huggingface.co/datasets/beans/resolve/main/data/validation.zip",
46
- "test": "https://huggingface.co/datasets/beans/resolve/main/data/test.zip",
47
- }
48
-
49
- _NAMES = ["angular_leaf_spot", "bean_rust", "healthy"]
50
-
51
-
52
- class Beans(datasets.GeneratorBasedBuilder):
53
- """Beans plant leaf images dataset."""
54
-
55
- def _info(self):
56
- return datasets.DatasetInfo(
57
- description=_DESCRIPTION,
58
- features=datasets.Features(
59
- {
60
- "image_file_path": datasets.Value("string"),
61
- "image": datasets.Image(),
62
- "labels": datasets.features.ClassLabel(names=_NAMES),
63
- }
64
- ),
65
- supervised_keys=("image", "labels"),
66
- homepage=_HOMEPAGE,
67
- citation=_CITATION,
68
- task_templates=[ImageClassification(image_column="image", label_column="labels")],
69
- )
70
-
71
- def _split_generators(self, dl_manager):
72
- data_files = dl_manager.download_and_extract(_URLS)
73
- return [
74
- datasets.SplitGenerator(
75
- name=datasets.Split.TRAIN,
76
- gen_kwargs={
77
- "files": dl_manager.iter_files([data_files["train"]]),
78
- },
79
- ),
80
- datasets.SplitGenerator(
81
- name=datasets.Split.VALIDATION,
82
- gen_kwargs={
83
- "files": dl_manager.iter_files([data_files["validation"]]),
84
- },
85
- ),
86
- datasets.SplitGenerator(
87
- name=datasets.Split.TEST,
88
- gen_kwargs={
89
- "files": dl_manager.iter_files([data_files["test"]]),
90
- },
91
- ),
92
- ]
93
-
94
- def _generate_examples(self, files):
95
- for i, path in enumerate(files):
96
- file_name = os.path.basename(path)
97
- if file_name.endswith(".jpg"):
98
- yield i, {
99
- "image_file_path": path,
100
- "image": path,
101
- "labels": os.path.basename(os.path.dirname(path)).lower(),
102
- }