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microsoft · Feb 24, 2025 · 817dec4 · 817dec4
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diff --git a/docs/api/datasets/non_geo_datasets.csv b/docs/api/datasets/non_geo_datasets.csv
@@ -16,7 +16,7 @@ Dataset,Task,Source,License,# Samples,# Classes,Size (px),Resolution (m),Bands
 `Digital Typhoon`_,"C, R",Himawari,"CC-BY-4.0","189,364",8,512,5000,Infrared
 `ETCI2021 Flood Detection`_,S,Sentinel-1,-,"66,810",2,256x256,5--20,SAR
 `EuroSAT`_,C,Sentinel-2,"MIT","27,000",10,64x64,10,MSI
-`EverWatch`_,OD,Aerial,"CC0 1.0 Universal","5,325",7,"1,500x1500p",0.01,RGB
+`EverWatch`_,OD,Aerial,"CC0-1.0","5,325",7,"1,500x1500p",0.01,RGB
 `FAIR1M`_,OD,Gaofen/Google Earth,"CC-BY-NC-SA-3.0","15,000",37,"1,024x1,024",0.3--0.8,RGB
 `Fields Of The World`_,"S,I",Sentinel-2,"Various","70795","2,3",256x256,10,MSI
 `FireRisk`_,C,NAIP Aerial,"CC-BY-NC-4.0","91,872",7,"320x320",1,RGB

diff --git a/torchgeo/datasets/everwatch.py b/torchgeo/datasets/everwatch.py
@@ -182,8 +182,8 @@ def _load_target(self, sample_df: pd.DataFrame) -> tuple[Tensor, Tensor]:
         Returns:
             bounding boxes and labels
         """
-        boxes = torch.Tensor(
-            sample_df[['xmin', 'ymin', 'xmax', 'ymax']].values.tolist()
+        boxes = torch.from_numpy(
+            sample_df[['xmin', 'ymin', 'xmax', 'ymax']].values
         ).float()
         labels = torch.Tensor(
             [self.class2idx[label] for label in sample_df['label'].tolist()]