diff --git a/.gitignore b/.gitignore
index b88d377..a1ac53a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,3 +7,4 @@ dm/__pycache__/
 nlp/__pycache__/
 __pycache__/
 pyWeb/
+research/imgcensor/model/
diff --git a/research/imgcensor/1.jpg b/research/imgcensor/1.jpg
new file mode 100644
index 0000000..6306b91
Binary files /dev/null and b/research/imgcensor/1.jpg differ
diff --git a/research/imgcensor/2.jpg b/research/imgcensor/2.jpg
new file mode 100644
index 0000000..5343f11
Binary files /dev/null and b/research/imgcensor/2.jpg differ
diff --git a/research/imgcensor/README.md b/research/imgcensor/README.md
index 6d1b122..bd085e9 100644
--- a/research/imgcensor/README.md
+++ b/research/imgcensor/README.md
@@ -13,4 +13,44 @@ Note: The dataset for pornography recognition is downloaded from [nsfw_data_scra
 | porn | 110003 |
 | sexy | 18299 |
 
+## Performance
+| Model | Accuracy | Precision | Recall |
+| :---: |:---: |:---: |:---: |
+| DenseNet121 | 93.31% | 90.68% | 89.72% |
 
+
+## Usage
+1. downloaded pretrained model from [Google Drive]()
+2. run [inference](./inference.py) by passing your own image
+3. model will return json results as:
+
+* Example 1 
+![1](./1.jpg)
+
+```json
+{"message": "success",
+ "results": [{"prob": 1.0, "type": "sexy"},
+             {"prob": 0.0, "type": "neutral"},
+             {"prob": 0.0, "type": "porn"},
+             {"prob": 0.0, "type": "hentai"},
+             {"prob": 0.0, "type": "drawings"}],
+ "status": 0}
+```
+
+Note: the returned result indicates that ```sexy``` has the highest 
+probability (prob=1.0)
+
+* Example 2 
+![2](./2.jpg)
+```json
+{"message": "success",
+ "results": [{"prob": 0.6981, "type": "neutral"},
+             {"prob": 0.1811, "type": "porn"},
+             {"prob": 0.1205, "type": "sexy"},
+             {"prob": 0.0002, "type": "hentai"},
+             {"prob": 0.0002, "type": "drawings"}],
+ "status": 0}
+```
+
+Note: the returned result indicates that ```neutral``` has the highest 
+probability (prob=0.6981)
\ No newline at end of file
diff --git a/research/imgcensor/inference.py b/research/imgcensor/inference.py
index feec3ea..d3c2fbe 100644
--- a/research/imgcensor/inference.py
+++ b/research/imgcensor/inference.py
@@ -11,7 +11,7 @@
 from torchvision import models
 
 sys.path.append('../')
-from research.imgcensor import cfg
+from research.imgcensor.cfg import cfg
 
 
 class NSFWEstimator:
@@ -20,14 +20,12 @@ class NSFWEstimator:
     """
 
     def __init__(self, pretrained_model_path):
-        model = models.resnet18(pretrained=True)
-        num_ftrs = model.fc.in_features
-        model.fc = nn.Linear(num_ftrs, cfg['out_num'])
+        model = models.densenet121(pretrained=True)
+        num_ftrs = model.classifier.in_features
+        model.classifier = nn.Linear(num_ftrs, cfg['out_num'])
 
-        model = model.float()
         device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 
-        # model = nn.DataParallel(model)
         model.load_state_dict(torch.load(pretrained_model_path))
 
         # if torch.cuda.device_count() > 1:
@@ -50,6 +48,13 @@ def __init__(self, pretrained_model_path):
         self.device = device
         self.model = model
         self.topK = 5
+        self.mapping = {
+            0: 'drawings',
+            1: 'hentai',
+            2: 'neutral',
+            3: 'porn',
+            4: 'sexy'
+        }
 
     def infer(self, img_file):
         img = Image.open(img_file)
@@ -69,19 +74,18 @@ def infer(self, img_file):
         outputs = self.model(img)
         outputs = F.softmax(outputs, dim=1)
 
-        # get TOP-K output labels and corresponding probabilities
         topK_prob, topK_label = torch.topk(outputs, self.topK)
         prob = topK_prob.to("cpu").detach().numpy().tolist()
 
         _, predicted = torch.max(outputs.data, 1)
 
         return {
-            'status': 0,
-            'message': 'success',
-            'results': [
+            "status": 0,
+            "message": "success",
+            "results": [
                 {
-                    'age': None,
-                    'prob': round(prob[0][i], 4)
+                    "prob": round(prob[0][i], 4),
+                    "type": self.mapping[int(topK_label[0][i].to("cpu"))],
                 } for i in range(self.topK)
             ]
         }
@@ -89,4 +93,4 @@ def infer(self, img_file):
 
 if __name__ == '__main__':
     nsfw = NSFWEstimator('./model/DenseNet121_NSFW.pth')
-    pprint(nsfw.infer('./1.jpg'))
+    pprint(nsfw.infer('./3.jpg'))