Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
10K - 100K
License:
| import os | |
| from datasets import DatasetInfo, GeneratorBasedBuilder, Split, SplitGenerator, Value, Image, ClassLabel | |
| _CATEGORIES = ["buildings", "forest", "glacier", "mountain", "sea", "street"] | |
| class IntelImageClassification(GeneratorBasedBuilder): | |
| def _info(self): | |
| return DatasetInfo( | |
| description="Intel Image Classification dataset with 6 natural scene categories.", | |
| features={ | |
| "image": Image(), | |
| "label": ClassLabel(names=_CATEGORIES), | |
| }, | |
| supervised_keys=("image", "label"), | |
| homepage="https://www.kaggle.com/datasets/puneet6060/intel-image-classification", | |
| # citation="Puneet Jindal, Intel Image Classification (Kaggle Dataset)", | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_dir = os.path.join(dl_manager.manual_dir, "data") | |
| return [ | |
| SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "seg_train")}), | |
| SplitGenerator(name=Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "seg_test")}), | |
| SplitGenerator(name=Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "seg_pred")}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| idx = 0 | |
| for label in sorted(os.listdir(filepath)): | |
| label_path = os.path.join(filepath, label) | |
| if not os.path.isdir(label_path): | |
| continue | |
| for img_file in sorted(os.listdir(label_path)): | |
| if img_file.lower().endswith((".jpg", ".jpeg", ".png")): | |
| yield idx, { | |
| "image": os.path.join(label_path, img_file), | |
| "label": label, | |
| } | |
| idx += 1 | |