update-to-match-accuracy

script.py

@@ -34,7 +34,7 @@ def preprocess(file_like):
     container = av.open(file_like)
     frames = []
     every = 10
-    for i,frame in enumerate(container.decode(video=0)):
+    for i, frame in enumerate(container.decode(video=0)):
         if i % every == 0:
             frame_array = frame.to_ndarray(format="rgb24")
             frame_tensor = torch.from_numpy(frame_array).permute(2, 0, 1).float()
@@ -83,11 +83,11 @@ for el in tqdm.tqdm(dataset_remote):
         with torch.no_grad():
             # soft decision (such as log likelihood score)
             # positive score correspond to synthetic prediction
-            # negative score correspond to pristine prediction
+            # negative score correspond to real prediction
             score = model(tensor[None].to(device)).cpu().item()
 
             # we require a hard decision to be submited. so you need to pick a threshold
-            pred = "generated" if score > model.threshold else "pristine"
+            pred = "generated" if score > model.threshold else "real"
 
         # append your prediction
         # "id" and "pred" are required. "score" will not be used in scoring but we encourage you to include it. We'll use it for analysis of the results