Working insect order classifier

Author: Aditya Jain

trapdata/api/demo.py

@@ -9,6 +9,7 @@
 
 from .models.classification import (
     APIMothClassifier,
+    InsectOrderClassifier,
     MothClassifierBinary,
     MothClassifierGlobal,
     MothClassifierPanama,
@@ -74,6 +75,12 @@ class ClassifierChoice:
         example_images_dir_names=["vermont", "panama", "denmark"],
         classifier=MothClassifierGlobal,
     ),
+    ClassifierChoice(
+        key=InsectOrderClassifier.get_key(),
+        tab_title="Insect Orders",
+        example_images_dir_names=["vermont", "panama", "denmark"],
+        classifier=InsectOrderClassifier,
+    ),
 ]
 
 

trapdata/api/models/classification.py

@@ -8,6 +8,7 @@
 from trapdata.ml.models.classification import (
     GlobalMothSpeciesClassifier,
     InferenceBaseClass,
+    InsectOrderClassifier2025,
     MothNonMothClassifier,
     PanamaMothSpeciesClassifier2024,
     PanamaMothSpeciesClassifierMixedResolution2023,
@@ -188,3 +189,7 @@ class MothClassifierTuringAnguilla(APIMothClassifier, TuringAnguillaSpeciesClass
 
 class MothClassifierGlobal(APIMothClassifier, GlobalMothSpeciesClassifier):
     pass
+
+
+class InsectOrderClassifier(APIMothClassifier, InsectOrderClassifier2025):
+    pass

trapdata/ml/models/classification.py

@@ -108,13 +108,14 @@ def forward(self, x):
 
 
 class ConvNeXtOrderClassifier(InferenceBaseClass):
-    """ConvNeXt based insect order classifier"""    
+    """ConvNeXt based insect order classifier"""
+
     input_size = 128
 
     def get_model(self):
         num_classes = len(self.category_map)
         model = timm.create_model(
-            "convnext_tiny.fb_in22k",
+            "convnext_tiny_in22k",
             weights=None,
             num_classes=num_classes,
         )
@@ -127,7 +128,6 @@ def get_model(self):
         model.eval()
         return model
 
-
     def _pad_to_square(self):
         """Padding transformation to make the image square"""
 
@@ -139,19 +139,17 @@ def _pad_to_square(self):
         else:
             return torchvision.transforms.Pad(padding=[0, 0, 0, 0])
 
-
     def get_transforms(self):
         mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
         return torchvision.transforms.Compose(
-            [   
-                self._pad_to_square(),
+            [
+                # self._pad_to_square(),
                 torchvision.transforms.Resize((self.input_size, self.input_size)),
                 torchvision.transforms.ToTensor(),
                 torchvision.transforms.Normalize(mean, std),
             ]
         )
 
-    
     def post_process_batch(self, output):
         predictions = torch.nn.functional.softmax(output, dim=1)
         predictions = predictions.cpu().numpy()