--- a
+++ b/NIH-Chest-X-ray-dataset.py
@@ -0,0 +1,224 @@
+# Copyright 2022 Cristóbal Alcázar
+#
+# 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.
+"""NIH Chest X-ray Dataset"""
+
+
+import os
+import datasets
+
+from requests import get
+from pandas import read_csv
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """\
+@inproceedings{Wang_2017,
+	doi = {10.1109/cvpr.2017.369},
+	url = {https://doi.org/10.1109%2Fcvpr.2017.369},
+	year = 2017,
+	month = {jul},
+	publisher = {{IEEE}
+},
+	author = {Xiaosong Wang and Yifan Peng and Le Lu and Zhiyong Lu and Mohammadhadi Bagheri and Ronald M. Summers},
+	title = {{ChestX}-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases},
+	booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})}
+}
+"""
+
+
+_DESCRIPTION = """\
+The NIH Chest X-ray dataset consists of 100,000 de-identified images of chest x-rays. The images are in PNG format.
+
+The data is provided by the NIH Clinical Center and is available through the NIH download site: https://nihcc.app.box.com/v/ChestXray-NIHCC
+"""
+
+
+_HOMEPAGE = "https://nihcc.app.box.com/v/chestxray-nihcc"
+
+
+_REPO = "https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/data"
+
+
+_IMAGE_URLS = [
+	f"{_REPO}/images/images_001.zip",
+	f"{_REPO}/images/images_002.zip",
+	f"{_REPO}/images/images_003.zip",
+	f"{_REPO}/images/images_004.zip",
+	f"{_REPO}/images/images_005.zip",
+	f"{_REPO}/images/images_006.zip",
+	f"{_REPO}/images/images_007.zip",
+	f"{_REPO}/images/images_008.zip",
+	f"{_REPO}/images/images_009.zip",
+    f"{_REPO}/images/images_010.zip",
+	f"{_REPO}/images/images_011.zip",
+	f"{_REPO}/images/images_012.zip"
+	#'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/dummy/0.0.0/images_001.tar.gz',
+	#'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/dummy/0.0.0/images_002.tar.gz'
+]
+
+
+_URLS = {
+	"train_val_list": f"{_REPO}/train_val_list.txt",
+	"test_list": f"{_REPO}/test_list.txt",
+	"labels": f"{_REPO}/Data_Entry_2017_v2020.csv",
+	"BBox": f"{_REPO}/BBox_List_2017.csv",
+	"image_urls": _IMAGE_URLS
+}
+
+
+_LABEL2IDX = {"No Finding": 0,
+	     "Atelectasis": 1,
+	     "Cardiomegaly": 2,
+	     "Effusion": 3,
+	     "Infiltration": 4,
+	     "Mass": 5,
+	     "Nodule": 6,
+	     "Pneumonia": 7,
+	     "Pneumothorax": 8,
+  	     "Consolidation": 9,
+	     "Edema": 10,
+	     "Emphysema": 11,
+	     "Fibrosis": 12,
+	     "Pleural_Thickening": 13,
+	     "Hernia": 14}
+
+
+_NAMES = list(_LABEL2IDX.keys())
+
+
+class ChestXray14Config(datasets.BuilderConfig):
+	"""NIH Image Chest X-ray14 configuration."""
+	
+	def __init__(self, name, **kwargs):
+	    super(ChestXray14Config, self).__init__(
+		version=datasets.Version("1.0.0"),
+		name=name,
+		description="NIH ChestX-ray14",
+		**kwargs,
+	    )
+
+
+
+class ChestXray14(datasets.GeneratorBasedBuilder):
+	"""NIH Image Chest X-ray14 dataset."""
+
+
+	BUILDER_CONFIGS = [
+		ChestXray14Config("image-classification"),
+		ChestXray14Config("object-detection"),
+	]
+
+	def _info(self):
+		if self.config.name == "image-classification":
+		    features = datasets.Features(
+                       {
+		        "image": datasets.Image(),
+  		        "labels": datasets.features.Sequence(
+                                     datasets.features.ClassLabel(
+                                        num_classes=len(_NAMES),
+                                        names=_NAMES
+                                     )
+                                 ),
+                       }
+                    )
+		    keys = ("image", "labels")
+
+
+		if self.config.name == "object-detection":
+		    features = datasets.Features(
+                       {
+			"image_id": datasets.Value("string"),
+			"patient_id": datasets.Value("int32"),
+		        "image": datasets.Image(),
+			"width": datasets.Value("int32"),
+			"height": datasets.Value("int32"),
+                       }
+                    )
+		    object_dict = {
+			"image_id": datasets.Value("string"), 
+			"area": datasets.Value("int64"),
+			"bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+			}
+		    features["objects"] = [object_dict]
+		    keys = ("image", "objects")
+
+
+
+		return datasets.DatasetInfo(
+		    description=_DESCRIPTION,
+		    features=features,
+		    supervised_keys=keys,
+		    homepage=_HOMEPAGE,
+		    citation=_CITATION,
+		)
+
+
+	def _split_generators(self, dl_manager):
+		# Get the image names that belong to the train-val dataset
+		logger.info("Downloading the train_val_list image names")
+		train_val_list = get(_URLS['train_val_list']).iter_lines()
+		train_val_list = set([x.decode('UTF8') for x in train_val_list])
+		logger.info(f"Check train_val_list: {train_val_list}")
+
+		# Create list for store the name of the images for each dataset
+		train_files = []
+		test_files = []
+		
+		# Download batches
+		data_files = dl_manager.download_and_extract(_URLS["image_urls"])
+
+		# Iterate trought image folder and check if they belong to
+		# the trainset or testset
+
+		for batch in data_files:
+		  logger.info(f"Batch for data_files: {batch}")
+		  path_files = dl_manager.iter_files(batch)
+		  for img in path_files:
+		    if os.path.basename(img) in train_val_list:
+		      train_files.append(img)
+		    else:
+		      test_files.append(img)
+		
+		return [
+		    datasets.SplitGenerator(
+			name=datasets.Split.TRAIN,
+			gen_kwargs={
+				"files": train_files
+			}
+
+		    ),
+		    datasets.SplitGenerator(
+			name=datasets.Split.TEST,
+			gen_kwargs={
+				"files": test_files
+			}
+		    )
+		]
+
+	def _generate_examples(self, files):
+
+		if self.config.name == "image-classification":
+		    # Read csv with image labels
+		    label_csv = read_csv(_URLS["labels"])
+		    for i, path in enumerate(files):
+		        file_name = os.path.basename(path)
+		        # Get image id to filter the respective row of the csv 	
+		        image_id = file_name
+		        image_labels = label_csv[label_csv["Image Index"] == image_id]["Finding Labels"].values[0].split("|")
+		        if file_name.endswith(".png"):
+		            yield i, {
+			        "image": path,
+			        "labels": image_labels,
+			    }
+