diff --git a/Dataturks-to-YOLOv3/README.md b/Dataturks-to-YOLOv3/README.md
new file mode 100644
index 0000000..4961994
--- /dev/null
+++ b/Dataturks-to-YOLOv3/README.md
@@ -0,0 +1,13 @@
+# Convert Dataturks json to YOLO format
+The ```convert.py``` script downloads the images and converts the annotations from the Dataturks json file to YOLO format.
+## Usage
+1. Clone or download this repository.
+2. Create folders for saving images and YOLO config files.
+3. Execute:
+```
+python3 convert.py -d <path/to/dataturks.json> -i <directory/to/save/downloaded/images/> -y <directory/to/save/yolo/config/files/>
+```
+Note: ```-v``` flag can be used for detailed output.
+## Output
+1. Downloaded images along with their annotations in the specified directory.
+2. YOLO config files ```train.txt, obj.names, obj.data, yolov3.cfg``` saved in the specified directory.
diff --git a/Dataturks-to-YOLOv3/convert.py b/Dataturks-to-YOLOv3/convert.py
new file mode 100644
index 0000000..6ce72b7
--- /dev/null
+++ b/Dataturks-to-YOLOv3/convert.py
@@ -0,0 +1,165 @@
+import os
+import argparse
+import json
+import requests
+
+def download_image(image_url, image_dir):
+    '''Downloads image from image_url to image_dir if the image doesn't exist.'''
+    
+    file_name = image_url.split('/')[-1]
+    file_path = os.path.join(image_dir, file_name)
+    if os.path.exists(file_path):
+    	verbose("[INFO]%s exists, skipping download" % file_name, v_flag)
+    	return file_path
+    
+    response = requests.get(image_url)
+    if response.status_code == 200:
+        with open(file_path, 'wb') as file:
+            file.write(response.content)
+        verbose("[INFO]Downloaded %s" % file_name, v_flag)
+        return file_path
+    else:
+        print('[WARNING]Unable to download image, skipping...')
+        return False
+
+def generate_annotation(label, data):
+    '''Generate annotation from the json file.'''
+    
+    image_width = data['imageWidth']
+    image_height = data['imageHeight']
+
+    #if four coordinates of the bounding box is given
+    if len(data['points']) == 4:
+        xmin = image_width * min(data['points'][0][0], data['points'][1][0], data['points'][2][0], data['points'][3][0])
+        ymin = image_height * min(data['points'][0][1], data['points'][1][1], data['points'][2][1], data['points'][3][1])
+        xmax = image_width * max(data['points'][0][0], data['points'][1][0], data['points'][2][0], data['points'][3][0])
+        ymax = image_height * max(data['points'][0][1], data['points'][1][1], data['points'][2][1], data['points'][3][1])
+
+    #if diagonal coordinates given
+    else:
+        xmin = int(data['points'][0]['x'] * image_width)
+        ymin = int(data['points'][0]['y'] * image_height)
+        xmax = int(data['points'][1]['x'] * image_width)
+        ymax = int(data['points'][1]['y'] * image_height)
+
+    #calculating coodinate ratios as required for training yolo
+    x_center = ((xmax + xmin) / 2.0) / image_width
+    y_center = ((ymax + ymin) / 2.0) / image_height
+    width = (xmax - xmin) / image_width
+    height = (ymax - ymin) / image_height
+
+    return ("%.6f %.6f %.6f %.6f\n"% (x_center, y_center, width, height))
+
+def convert_to_yolo_annotation():
+    classes = []
+    train_txt = []
+    with open(dataturks_json_path, 'r') as file:
+        lines = file.readlines()
+    for line in lines:
+        data = json.loads(line)
+        if data['annotation'] == None:
+            continue
+
+        file_path = download_image(data['content'], image_dir)
+
+        if not file_path:
+            continue
+
+        annotation = ''
+        
+        for item in data['annotation']:
+            if item['label'] == None:
+                continue
+            
+            labels = item['label']
+            if not isinstance(labels, list):
+                labels = [labels]
+                
+            for label in labels:
+                if label not in classes:
+                    classes.append(label)
+
+                annotation = annotation + str(classes.index(label)) + ' ' + generate_annotation(label, item)
+
+        train_txt.append(str(os.path.abspath(file_path)) + '\n')
+
+        annotation_file = '.'.join(file_path.split('.')[:-1]) + '.txt'
+
+        with open(annotation_file, 'w') as f:
+            f.write(annotation)
+        verbose("[INFO]%s file generated." % annotation_file, v_flag)
+            
+    with open(os.path.join(yolo_dir, 'train.txt'), 'w') as file:
+    	file.writelines(train_txt)
+    verbose("[INFO]train.txt file generated.", v_flag)
+                
+    return classes
+
+def generate_yolo_cfg_files(classes):
+	
+    with open(os.path.join(yolo_dir, 'obj.names'), 'w') as file:
+        for item in classes:
+            file.write(item + '\n')
+    verbose("[INFO]obj.names file generated.", v_flag)
+
+    with open(os.path.join(yolo_dir, 'obj.data'), 'w') as file:
+        file.write('classes = %s\ntrain = %s\nnames = %s\nbackup = %s' %
+                   (str(len(classes)),
+                    str(os.path.join(os.path.abspath(yolo_dir), 'train.txt')),
+                    str(os.path.join(os.path.abspath(yolo_dir), 'obj.names')),
+                    str(os.path.join(os.path.abspath(yolo_dir), 'backup/'))
+                   )
+        )
+    verbose("[INFO]obj.data file generated.", v_flag)
+
+    n_classes = len(classes)
+
+    n_filters = (n_classes + 5) * 3
+
+    with open(os.path.join(yolo_dir, 'yolov3.cfg'), 'w') as file:
+        with open('yolov3.cfg.template') as template:
+            lines = template.readlines()
+        for i in range(len(lines)):
+            lines[i] = lines[i].replace('#FILTER#', str(n_filters))
+            lines[i] = lines[i].replace('#CLASS#', str(n_classes))
+        file.writelines(lines)
+    verbose("[INFO]yolov3.cfg file generated.", v_flag)
+
+        
+def main():
+    if not os.path.isdir(image_dir):
+        print('[ERROR]The directory %s does not exist' % os.path.abspath(image_dir))
+        return
+    if not os.path.exists(dataturks_json_path):
+        print('[ERROR]The specified json file does not exitst')
+        return
+    if not os.path.isdir(yolo_dir):
+        print('[ERROR]The directory %s does not exist' % os.path.abspath(yolo_dir))
+        return
+    classes = convert_to_yolo_annotation()
+    generate_yolo_cfg_files(classes)
+    
+
+def arg_parser():
+    parser = argparse.ArgumentParser(description = 'Converts Dataturks JSON format to yolo-darknet format.')
+    parser.add_argument('-v', help = 'Verbose output.', action = 'store_true')
+    parser.add_argument('-d', '--dataturks_json_path', required = True, help = 'Path to the Dataturks JSON file.')
+    parser.add_argument('-i', '--image_dir', required = True, help = 'Path to the directory where the images with annotations will be stored.')
+    parser.add_argument('-y', '--yolo_dir', required = True, help = 'Path to the directory where the files for training YOLO will be stored.')
+    return parser.parse_args()
+    
+def verbose(message, v_flag):
+	if v_flag == True:
+		print(message)
+
+if __name__ == '__main__':
+    args = arg_parser()
+    global dataturks_json_path
+    global image_dir
+    global yolo_dir
+    global v_flag
+    dataturks_json_path = args.dataturks_json_path
+    image_dir = args.image_dir
+    yolo_dir = args.yolo_dir
+    v_flag = args.v
+    main()
diff --git a/Dataturks-to-YOLOv3/yolov3.cfg.template b/Dataturks-to-YOLOv3/yolov3.cfg.template
new file mode 100644
index 0000000..eff4489
--- /dev/null
+++ b/Dataturks-to-YOLOv3/yolov3.cfg.template
@@ -0,0 +1,789 @@
+[net]
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=16
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+######################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=#FILTER#
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=#CLASS#
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 61
+
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=#FILTER#
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=#CLASS#
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 36
+
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=#FILTER#
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=#CLASS#
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+