diff --git a/src/eynollah/training/models.py b/src/eynollah/training/models.py
index 3b38fe8..011c614 100644
--- a/src/eynollah/training/models.py
+++ b/src/eynollah/training/models.py
@@ -12,7 +12,10 @@ from tensorflow.keras.regularizers import l2
 ###projection_dim = 64
 ##transformer_layers = 2#8
 ##num_heads = 1#4
-resnet50_Weights_path = './pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
+RESNET50_WEIGHTS_PATH = './pretrained_model/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'
+RESNET50_WEIGHTS_URL = ('https://github.com/fchollet/deep-learning-models/releases/download/v0.2/'
+                        'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')
+
 IMAGE_ORDERING = 'channels_last'
 MERGE_AXIS = -1
 
@@ -242,7 +245,7 @@ def resnet50_unet_light(n_classes, input_height=224, input_width=224, taks="segm
     f5 = x
 
     if pretraining:
-        model = Model(img_input, x).load_weights(resnet50_Weights_path)
+        model = Model(img_input, x).load_weights(RESNET50_WEIGHTS_PATH)
 
     v512_2048 = Conv2D(512, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(f5)
     v512_2048 = (BatchNormalization(axis=bn_axis))(v512_2048)
@@ -343,7 +346,7 @@ def resnet50_unet(n_classes, input_height=224, input_width=224, task="segmentati
     f5 = x
 
     if pretraining:
-        Model(img_input, x).load_weights(resnet50_Weights_path)
+        Model(img_input, x).load_weights(RESNET50_WEIGHTS_PATH)
 
     v1024_2048 = Conv2D(1024, (1, 1), padding='same', data_format=IMAGE_ORDERING, kernel_regularizer=l2(weight_decay))(
         f5)
@@ -442,7 +445,7 @@ def vit_resnet50_unet(n_classes, patch_size_x, patch_size_y, num_patches, mlp_he
     f5 = x 
     
     if pretraining:
-        model = Model(inputs, x).load_weights(resnet50_Weights_path)
+        model = Model(inputs, x).load_weights(RESNET50_WEIGHTS_PATH)
 
     #num_patches = x.shape[1]*x.shape[2]
     
@@ -590,7 +593,7 @@ def vit_resnet50_unet_transformer_before_cnn(n_classes, patch_size_x, patch_size
     f5 = x 
     
     if pretraining:
-        model = Model(encoded_patches, x).load_weights(resnet50_Weights_path)
+        model = Model(encoded_patches, x).load_weights(RESNET50_WEIGHTS_PATH)
 
     v1024_2048 = Conv2D( 1024 , (1, 1), padding='same', data_format=IMAGE_ORDERING,kernel_regularizer=l2(weight_decay))(x)
     v1024_2048 = (BatchNormalization(axis=bn_axis))(v1024_2048)
@@ -690,7 +693,7 @@ def resnet50_classifier(n_classes,input_height=224,input_width=224,weight_decay=
     f5 = x 
 
     if pretraining:
-        Model(img_input, x).load_weights(resnet50_Weights_path)
+        Model(img_input, x).load_weights(RESNET50_WEIGHTS_PATH)
 
     x = AveragePooling2D((7, 7), name='avg_pool')(x)
     x = Flatten()(x)
@@ -746,7 +749,7 @@ def machine_based_reading_order_model(n_classes,input_height=224,input_width=224
     x1 = identity_block(x1, 3, [512, 512, 2048], stage=5, block='c')
     
     if pretraining:
-        Model(img_input , x1).load_weights(resnet50_Weights_path)
+        Model(img_input , x1).load_weights(RESNET50_WEIGHTS_PATH)
     
     x1 = AveragePooling2D((7, 7), name='avg_pool1')(x1)
     flattened = Flatten()(x1)
diff --git a/src/eynollah/training/train.py b/src/eynollah/training/train.py
index 7ee63f9..6353474 100644
--- a/src/eynollah/training/train.py
+++ b/src/eynollah/training/train.py
@@ -2,6 +2,7 @@ import os
 import sys
 import json
 
+import requests
 import click
 
 from eynollah.training.metrics import (
@@ -15,7 +16,9 @@ from eynollah.training.models import (
     resnet50_classifier,
     resnet50_unet,
     vit_resnet50_unet,
-    vit_resnet50_unet_transformer_before_cnn
+    vit_resnet50_unet_transformer_before_cnn,
+    RESNET50_WEIGHTS_PATH,
+    RESNET50_WEIGHTS_URL
 )
 from eynollah.training.utils import (
     data_gen,
@@ -80,6 +83,12 @@ def get_dirs_or_files(input_data):
         assert os.path.isdir(labels_input), "{} is not a directory".format(labels_input)
     return image_input, labels_input
 
+def download_file(url, path):
+    with open(path, 'wb') as f:
+        with requests.get(url, stream=True) as r:
+            r.raise_for_status()
+            for data in r.iter_content(chunk_size=4096):
+                f.write(data)
 
 ex = Experiment(save_git_info=False)
 
@@ -163,6 +172,10 @@ def run(_config, n_classes, n_epochs, input_height,
         transformer_patchsize_x, transformer_patchsize_y,
         transformer_num_patches_xy, backbone_type, save_interval, flip_index, dir_eval, dir_output,
         pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds, dir_rgb_foregrounds):
+
+    if pretraining and not os.path.isfile(RESNET50_WEIGHTS_PATH):
+        print("downloading RESNET50 pretrained weights to", RESNET50_WEIGHTS_PATH)
+        download_file(RESNET50_WEIGHTS_URL, RESNET50_WEIGHTS_PATH)
     
     if dir_rgb_backgrounds:
         list_all_possible_background_images = os.listdir(dir_rgb_backgrounds)