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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import csv
import json
import os
import datasets
from datasets.tasks import ImageClassification
_CITATION = """\
@article{FeiFei2004LearningGV,
title={Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories},
author={Li Fei-Fei and Rob Fergus and Pietro Perona},
journal={Computer Vision and Pattern Recognition Workshop},
year={2004},
}
"""
_DESCRIPTION = """\
Pictures of objects belonging to 101 categories.
About 40 to 800 images per category.
Most categories have about 50 images.
Collected in September 2003 by Fei-Fei Li, Marco Andreetto, and Marc'Aurelio Ranzato.
The size of each image is roughly 300 x 200 pixels.
"""
_HOMEPAGE = "https://data.caltech.edu/records/20086"
_LICENSE = "CC BY 4.0"
_DATA_URL = "brand_new_data/caltech-101.zip"
# _DATA_URL = "brand_new_data/caltech-101/101_ObjectCategories.tar.gz"
_NAMES = [
"accordion",
"airplanes",
"anchor",
"ant",
"background_google",
"barrel",
"bass",
"beaver",
"binocular",
"bonsai",
"brain",
"brontosaurus",
"buddha",
"butterfly",
"camera",
"cannon",
"car_side",
"ceiling_fan",
"cellphone",
"chair",
"chandelier",
"cougar_body",
"cougar_face",
"crab",
"crayfish",
"crocodile",
"crocodile_head",
"cup",
"dalmatian",
"dollar_bill",
"dolphin",
"dragonfly",
"electric_guitar",
"elephant",
"emu",
"euphonium",
"ewer",
"faces",
"faces_easy",
"ferry",
"flamingo",
"flamingo_head",
"garfield",
"gerenuk",
"gramophone",
"grand_piano",
"hawksbill",
"headphone",
"hedgehog",
"helicopter",
"ibis",
"inline_skate",
"joshua_tree",
"kangaroo",
"ketch",
"lamp",
"laptop",
"leopards",
"llama",
"lobster",
"lotus",
"mandolin",
"mayfly",
"menorah",
"metronome",
"minaret",
"motorbikes",
"nautilus",
"octopus",
"okapi",
"pagoda",
"panda",
"pigeon",
"pizza",
"platypus",
"pyramid",
"revolver",
"rhino",
"rooster",
"saxophone",
"schooner",
"scissors",
"scorpion",
"sea_horse",
"snoopy",
"soccer_ball",
"stapler",
"starfish",
"stegosaurus",
"stop_sign",
"strawberry",
"sunflower",
"tick",
"trilobite",
"umbrella",
"watch",
"water_lilly",
"wheelchair",
"wild_cat",
"windsor_chair",
"wrench",
"yin_yang",
]
_TRAIN_POINTS_PER_CLASS = 30
class Caltech101(datasets.GeneratorBasedBuilder):
"""Caltech 101 dataset."""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"img": datasets.Image(),
"label": datasets.features.ClassLabel(names=_NAMES),
}
),
supervised_keys=("img", "label"),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
task_templates=ImageClassification(
image_column="img", label_column="label"
),
)
def _split_generators(self, dl_manager):
# ----- Work in progress here -----
data_dir = dl_manager.download_and_extract(_DATA_URL)
files = dl_manager.iter_files(data_dir)
# ---------------------------------
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir, # TODO: change accordingly
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir, # TODO: change accordingly
"split": "test",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
# TODO
pass
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