Upload 3 files

app.py

@@ -6,9 +6,10 @@ from PIL import Image
 # 0     1         2                    3         4
 # Name, point-th, half-false-positive, balanced, half-true-positive
 presets = {  #
+    ("General (NoSl)", 0.5, 0.55, 0.87, 0.94),  #
+    ("General", 0.5, 0.55, 0.87, 0.94),  #
     ("LatentDiffusion", 0.97, 0.65, 0.78, 0.88),  #
     ("SdXl", 0.6, 0.49, 0.83, 0.89),  #
-    ("General", 0.5, 0.55, 0.87, 0.94),  #
     ("DALL-E", 0.03, 0.8932, 0.9876, 0.999),  #
 }
 # These hyperparameters are based on the model's performance on the validation set.
@@ -16,12 +17,16 @@ presets = {  #
 if torch.cuda.is_available():
     device = torch.device('cuda')
     model = torch.load('m3_80.pth').to(device)
-    refiner = torch.load('model2_p4.pth').to(device)
+    refiner1 = torch.load('susu_s1.pth').to(device)
+    refiner2 = torch.load('susu_s2.pth').to(device)
 else:
     device = torch.device('cpu')
     model = torch.load('m3_80.pth', map_location=device)
-    refiner = torch.load('model2_p4.pth', map_location=device)
+    refiner1 = torch.load('susu_s1.pth', map_location=device)
+    refiner2 = torch.load('susu_s2.pth', map_location=device)
+
 
+# ! susu_s1 model is not an e2e model thus you shall get nearly no accuracy if you use it alone !
 
 def process_image(image, p_name):
     image = image.convert('RGB')
@@ -41,11 +46,13 @@ def process_image(image, p_name):
             np_img = tmp[begin_h:begin_h + 512, begin_w:begin_w + 512]
             torch_img = torch.tensor(np_img).float().unsqueeze(0) / 255.0
             torch_img = torch_img.to(device)
-            y1 = model(torch_img).detach().cpu().numpy().squeeze()
-            y1 = np.where(y1 > 0.5, 1, 0)
+            y1 = refiner1(torch_img).detach().cpu().numpy().squeeze()
+            y1 = np.where(y1 > 0.837295, 1, 0)  # @See -> tr_2 block -1
             x2 = torch.tensor(y1).float().unsqueeze(0).to(device)
-            y2 = float(refiner(x2))
+            y2 = float(refiner2(x2))
             y2 = min(max(y2, 0), 1)
+            if 0.2 < y2 < 0.8:
+                return "SlGeneral : No Comment, sorry! / P_A=" + str(round(y2 * 100, 2)) + " / Rule-based", None
             if y2 > 0.5:
                 return "SlGeneral : AI Generated / R=" + str(round(y2 * 100, 2)) + " / Rule-based", None
             else:

susu_s1.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60e1376b25bb0eceda123c7772294b124b7ef92a58d7772f6c070a2e7bffa89e
+size 1468627

susu_s2.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:59ccdd8329f2380c6e82cd16d787adf596d5f4aded492605abaa8746a70c347e
+size 11726371