diff --git a/basenet/vgg16_bn.py b/basenet/vgg16_bn.py
index f3f21a7..d207bc7 100644
--- a/basenet/vgg16_bn.py
+++ b/basenet/vgg16_bn.py
@@ -4,7 +4,10 @@
 import torch.nn as nn
 import torch.nn.init as init
 from torchvision import models
-from torchvision.models.vgg import model_urls
+
+model_urls = {
+    'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
+}
 
 def init_weights(modules):
     for m in modules:
diff --git a/bbox.py b/bbox.py
new file mode 100644
index 0000000..bfdcaf6
--- /dev/null
+++ b/bbox.py
@@ -0,0 +1,156 @@
+# -*- coding: utf-8 -*-
+import sys
+import os
+import time
+import argparse
+
+import torch
+import torch.nn as nn
+import torch.backends.cudnn as cudnn
+from torch.autograd import Variable
+
+from PIL import Image
+
+import cv2
+from skimage import io
+import numpy as np
+import craft_utils
+import imgproc
+import file_utils
+import json
+import zipfile
+
+from craft import CRAFT
+
+from collections import OrderedDict
+def copyStateDict(state_dict):
+    if list(state_dict.keys())[0].startswith("module"):
+        start_idx = 1
+    else:
+        start_idx = 0
+    new_state_dict = OrderedDict()
+    for k, v in state_dict.items():
+        name = ".".join(k.split(".")[start_idx:])
+        new_state_dict[name] = v
+    return new_state_dict
+
+def str2bool(v):
+    return v.lower() in ("yes", "y", "true", "t", "1")
+
+# define arguments
+class Args:
+    test_folder = '/data/'
+    trained_model = 'craft_mlt_25k.pth'
+    text_threshold = 0.7
+    low_text = 0.4
+    link_threshold = 0.4
+    cuda = False
+    canvas_size = 1280
+    mag_ratio = 1.5
+    poly = False
+    show_time = False
+    refiner_model = 'craft_refiner_CTW1500.pth'
+    refine=False
+
+args = Args()
+
+
+""" For test images in a folder """
+image_list, _, _ = file_utils.get_files(args.test_folder)
+
+result_folder = './result/'
+if not os.path.isdir(result_folder):
+    os.mkdir(result_folder)
+
+def test_net(net, image, text_threshold, link_threshold, low_text, cuda, poly, refine_net=None):
+    t0 = time.time()
+
+    # resize
+    img_resized, target_ratio, size_heatmap = imgproc.resize_aspect_ratio(image, args.canvas_size, interpolation=cv2.INTER_LINEAR, mag_ratio=args.mag_ratio)
+    ratio_h = ratio_w = 1 / target_ratio
+
+    # preprocessing
+    x = imgproc.normalizeMeanVariance(img_resized)
+    x = torch.from_numpy(x).permute(2, 0, 1)    # [h, w, c] to [c, h, w]
+    x = Variable(x.unsqueeze(0))                # [c, h, w] to [b, c, h, w]
+    if cuda:
+        x = x.cuda()
+
+    # forward pass
+    with torch.no_grad():
+        y, feature = net(x)
+
+    # make score and link map
+    score_text = y[0,:,:,0].cpu().data.numpy()
+    score_link = y[0,:,:,1].cpu().data.numpy()
+
+    # refine link
+    if refine_net is not None:
+        with torch.no_grad():
+            y_refiner = refine_net(y, feature)
+        score_link = y_refiner[0,:,:,0].cpu().data.numpy()
+
+    t0 = time.time() - t0
+    t1 = time.time()
+
+    # Post-processing
+    boxes, polys = craft_utils.getDetBoxes(score_text, score_link, text_threshold, link_threshold, low_text, poly)
+
+    # coordinate adjustment
+    boxes = craft_utils.adjustResultCoordinates(boxes, ratio_w, ratio_h)
+    polys = craft_utils.adjustResultCoordinates(polys, ratio_w, ratio_h)
+    for k in range(len(polys)):
+        if polys[k] is None: polys[k] = boxes[k]
+
+    t1 = time.time() - t1
+
+    # render results (optional)
+    render_img = score_text.copy()
+    render_img = np.hstack((render_img, score_link))
+    ret_score_text = imgproc.cvt2HeatmapImg(render_img)
+
+    if args.show_time : print("\ninfer/postproc time : {:.3f}/{:.3f}".format(t0, t1))
+
+    return boxes, polys, ret_score_text
+
+
+
+# if __name__ == '__main__':
+# load net
+net = CRAFT()     # initialize
+
+print('Loading weights from checkpoint (' + args.trained_model + ')')
+if args.cuda:
+    net.load_state_dict(copyStateDict(torch.load(args.trained_model)))
+else:
+    net.load_state_dict(copyStateDict(torch.load(args.trained_model, map_location='cpu')))
+
+if args.cuda:
+    net = net.cuda()
+    net = torch.nn.DataParallel(net)
+    cudnn.benchmark = False
+
+net.eval()
+
+# LinkRefiner
+refine_net = None
+if args.refine:
+    from refinenet import RefineNet
+    refine_net = RefineNet()
+    print('Loading weights of refiner from checkpoint (' + args.refiner_model + ')')
+    if args.cuda:
+        refine_net.load_state_dict(copyStateDict(torch.load(args.refiner_model)))
+        refine_net = refine_net.cuda()
+        refine_net = torch.nn.DataParallel(refine_net)
+    else:
+        refine_net.load_state_dict(copyStateDict(torch.load(args.refiner_model, map_location='cpu')))
+
+    refine_net.eval()
+    args.poly = True
+
+t = time.time()
+
+def getbboxes(image_path):
+    image = imgproc.loadImage(image_path)
+    bboxes, p, s=test_net(net, image, args.text_threshold, args.link_threshold, args.low_text, args.cuda, args.poly, refine_net)
+    return bboxes
\ No newline at end of file