Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
1K<n<10K
Tags:
biology
License:
File size: 1,441 Bytes
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import os
from kaggle.api.kaggle_api_extended import KaggleApi
import shutil
import tarfile
data_dir = 'data/'
kaggle_api = KaggleApi()
kaggle_api.authenticate()
kaggle_api.dataset_download_files('gpiosenka/100-bird-species', path=data_dir, unzip=True)
# There is a bug in the dataset, where one of the folders is named "PARAKETT AUKLET" instead of "PARAKETT AUKLET"
# We fix it by adding a space to the valid folder, because train and test are wrong and also the names in the labels file
# Fixing the path
fault_path = os.path.join(data_dir, 'valid', 'PARAKETT AUKLET')
correct_path = os.path.join(data_dir, 'valid', 'PARAKETT AUKLET')
shutil.rmtree(correct_path, ignore_errors=True)
shutil.move(fault_path, correct_path)
# Compressing the train directory
with tarfile.open(os.path.join(data_dir, 'train.tar.gz'), 'w:gz') as tar:
tar.add(os.path.join(data_dir, 'train'), arcname=os.path.basename(os.path.join(data_dir, 'train')))
# Compressing the test directory
with tarfile.open(os.path.join(data_dir, 'test.tar.gz'), 'w:gz') as tar:
tar.add(os.path.join(data_dir, 'test'), arcname=os.path.basename(os.path.join(data_dir, 'test')))
# Compressing the valid directory
with tarfile.open(os.path.join(data_dir, 'valid.tar.gz'), 'w:gz') as tar:
tar.add(os.path.join(data_dir, 'valid'), arcname=os.path.basename(os.path.join(data_dir, 'valid')))
os.remove(os.path.join(data_dir, 'EfficientNetB0-525-(224 X 224)- 98.97.h5'))
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