2025-08-22 14:42 🚀

app.py

@@ -33,18 +33,22 @@ pretrained_models = [
     "ZIP-S @ ShanghaiTech A @ MAE", "ZIP-S @ ShanghaiTech A @ NAE",
     "ZIP-S @ ShanghaiTech B @ MAE", "ZIP-S @ ShanghaiTech B @ NAE",
     "ZIP-S @ UCF-QNRF @ MAE", "ZIP-S @ UCF-QNRF @ NAE",
+    "ZIP-S @ NWPU-Crowd @ MAE", "ZIP-S @ NWPU-Crowd @ NAE",
     "━━━━━━━━━━━━━━━━━━━━━━━━━━━━",
     "ZIP-T @ ShanghaiTech A @ MAE", "ZIP-T @ ShanghaiTech A @ NAE",
     "ZIP-T @ ShanghaiTech B @ MAE", "ZIP-T @ ShanghaiTech B @ NAE",
     "ZIP-T @ UCF-QNRF @ MAE", "ZIP-T @ UCF-QNRF @ NAE",
+    "ZIP-T @ NWPU-Crowd @ MAE", "ZIP-T @ NWPU-Crowd @ NAE",
     "━━━━━━━━━━━━━━━━━━━━━━━━━━━━",
     "ZIP-N @ ShanghaiTech A @ MAE", "ZIP-N @ ShanghaiTech A @ NAE",
     "ZIP-N @ ShanghaiTech B @ MAE", "ZIP-N @ ShanghaiTech B @ NAE",
     "ZIP-N @ UCF-QNRF @ MAE", "ZIP-N @ UCF-QNRF @ NAE",
+    "ZIP-N @ NWPU-Crowd @ MAE", "ZIP-N @ NWPU-Crowd @ NAE",
     "━━━━━━━━━━━━━━━━━━━━━━━━━━━━",
     "ZIP-P @ ShanghaiTech A @ MAE", "ZIP-P @ ShanghaiTech A @ NAE",
     "ZIP-P @ ShanghaiTech B @ MAE", "ZIP-P @ ShanghaiTech B @ NAE",
     "ZIP-P @ UCF-QNRF @ MAE", "ZIP-P @ UCF-QNRF @ NAE",
+    "ZIP-P @ NWPU-Crowd @ MAE", "ZIP-P @ NWPU-Crowd @ NAE",
 ]
 
 # -----------------------------
@@ -346,7 +350,7 @@ def predict(image: Image.Image, variant_dataset_metric: str):
         elif dataset_name == "qnrf":
             loaded_model.input_size = 672
         elif dataset_name == "nwpu":
-            loaded_model.input_size = 672
+            loaded_model.input_size = 896
     elif isinstance(loaded_model.input_size, (list, tuple)):
         loaded_model.input_size = loaded_model.input_size[0]  # Use the first element if it's a list or tuple
     else: