diff --git a/config.py b/config.py
index 4e866a2..6b4159c 100644
--- a/config.py
+++ b/config.py
@@ -10,16 +10,15 @@
 
 # data settings
 dataset_path = "zerobox_dataset"
-class_name = "zerobox_class"
-modelname = "zerobox_test"
+class_name = "zerobox-2009-5"
+modelname = "zerobox-2009-5"
 
-# img_size = (448, 448)
-img_size = (480, 270)
+img_size = (448, 448)
 img_dims = [3] + list(img_size)
 add_img_noise = 0.01
 
 # transformation settings
-transf_rotations = False
+transf_rotations = True
 transf_brightness = 0.0
 transf_contrast = 0.0
 transf_saturation = 0.0
@@ -36,7 +35,7 @@
 
 # dataloader parameters
 n_transforms = 1 # number of transformations per sample in training
-n_transforms_test = 64 # number of transformations per sample in testing
+n_transforms_test = 16  # number of transformations per sample in testing
 batch_size = 1 # actual batch size is this value multiplied by n_transforms(_test)
 batch_size_test = 1 # batch_size * n_transforms // n_transforms_test
 
diff --git a/runTest.py b/runTest.py
index 6ad53ed..956519d 100644
--- a/runTest.py
+++ b/runTest.py
@@ -16,8 +16,9 @@
 from utils import *
 from localization import export_gradient_maps
 from torch.autograd import Variable
+from sklearn.metrics import roc_curve
 
-def test(model, test_loader):
+def test(model, test_loader, target_threshold):
     print("Running test")
     optimizer = torch.optim.Adam(model.nf.parameters(), lr=c.lr_init, betas=(0.8, 0.8), eps=1e-04, weight_decay=1e-5)
     # score_obs = Score_Observer('AUROC')
@@ -56,10 +57,22 @@ def test(model, test_loader):
     print(f"test_labels={test_labels}, is_anomaly={is_anomaly},anomaly_score={anomaly_score}")
     # score_obs.update(roc_auc_score(is_anomaly, anomaly_score), epoch,
     #                 print_score=c.verbose or epoch == c.meta_epochs - 1)
+    # Code to calculate the accurarcy from Lihang's code
+    is_anomaly_detected = np.array([0 if l < target_threshold else 1 for l in anomaly_score])
 
-    if c.grad_map_viz:
-        print("saving gradient maps...")
-        export_gradient_maps(model, test_loader, optimizer, -1)
+    # calculate test accuracy
+    error_count = 0
+    for i in range(len(is_anomaly)):
+        if is_anomaly[i] != is_anomaly_detected[i]:
+            error_count += 1
+
+    test_accuracy = 1 - float(error_count) / len(is_anomaly)
+
+    print(f"n_transforms_test = {c.n_transforms_test}, target_threshold={target_threshold}, test_accuracy={test_accuracy}")
+
+    # if c.grad_map_viz:
+    #     print("saving gradient maps...")
+    #     export_gradient_maps(model, test_loader, optimizer, -1)
 
 def load_testloader(data_dir_test):
     def target_transform(target):
@@ -80,8 +93,8 @@ def target_transform(target):
             class_idx += 1
 
     augmentative_transforms = []
-    # if c.transf_rotations:
-    #     augmentative_transforms += [transforms.RandomRotation(180)]
+    if c.transf_rotations:
+        augmentative_transforms += [transforms.RandomRotation(180)]
     if c.transf_brightness > 0.0 or c.transf_contrast > 0.0 or c.transf_saturation > 0.0:
         augmentative_transforms += [transforms.ColorJitter(brightness=c.transf_brightness, contrast=c.transf_contrast,
                                                            saturation=c.transf_saturation)]
@@ -100,14 +113,15 @@ def target_transform(target):
 # train_set, test_set = load_datasets(c.dataset_path, c.class_name)
 # _, test_loader = make_dataloaders(train_set, test_set)
 
-test_loader = load_testloader("group15B.avi/")
-# model = torch.load("../zerobox-v2/zerobox_differnet_model.pt", map_location=torch.device('cpu'))
-model = torch.load("models/zerobox_test.pt", map_location=torch.device('cpu'))
+test_loader = load_testloader("dataset/group15B.avi.Products/test")
+model = torch.load("models/zerobox-2010-1-black-yolo_0_0.10_0.05_0.10_0.05_0.9980.pth", map_location=torch.device('cpu'))
+# model = torch.load("models/zerobox_test.pt", map_location=torch.device('cpu'))
+target_threshold= 3.5129451751708984 
 
 print("starting to run tests after loaded model and test dataset")
 time_start = time.time()
 # model = load_model(c.modelname)
-test(model, test_loader)
+test(model, test_loader, target_threshold)
 time_end = time.time()
 time_c = time_end - time_start  # 运行所花时间
 print("time cost: {:f} s".format(time_c))