--- a
+++ b/datasets/dataset_classifier.py
@@ -0,0 +1,50 @@
+import os,sys
+import numpy as np
+from PIL import Image as PILImage
+import torch
+import torch.nn.functional as F
+from torch.utils import data as data
+from torchvision import transforms as transforms
+
+# dataset for sign detection and char detection
+class COVID_CT_DATA(data.Dataset):
+
+     def __init__(self, **kwargs):           
+         super(COVID_CT_DATA).__init__()
+         self.stage = kwargs['stage']
+         # this returns the path to data dir
+         self.data = kwargs['data']         
+         self.fs = sorted(os.listdir(self.data))
+         self.size = kwargs['img_size']
+         # this returns the path to 
+         self.img_fname = None
+
+     def transform_img(self, img):
+         # Faster R-CNN does the normalization
+         t_ = transforms.Compose([
+                             #transforms.ToPILImage(),
+                             transforms.Resize(self.size),
+                             transforms.ToTensor(),
+                             ])
+         img = t_(img)
+         return img
+
+     def load_img_label(self, idx):
+         lab=torch.zeros(3, dtype=torch.float)
+         lab[int(self.fs[idx].split('_')[0])] = 1
+         im = PILImage.open(os.path.join(self.data, self.fs[idx]))
+         if im.mode !='RGB':
+            im = im.convert(mode='RGB')
+         im = self.transform_img(im)
+         return im, lab
+
+     #'magic' method: size of the dataset
+     def __len__(self):
+         return len(os.listdir(self.data))
+
+     # return one datapoint
+     def __getitem__(self, idx):
+         X,y = self.load_img_label(idx)
+         return X,y
+
+