diff --git a/fiftyone/utils/super_gradients.py b/fiftyone/utils/super_gradients.py
index 810d722b47..9d0242afb0 100644
--- a/fiftyone/utils/super_gradients.py
+++ b/fiftyone/utils/super_gradients.py
@@ -49,13 +49,7 @@ def convert_super_gradients_model(model):
 
 
 def _convert_yolo_nas_detection_model(model):
-    config_model = {
-        "model": model,
-        "labels_path": "{{eta-resources}}/ms-coco-labels.txt",
-        "output_processor_cls": "fiftyone.utils.torch.ClassifierOutputProcessor",
-        "raw_inputs": True,
-    }
-
+    config_model = {"model": model, "raw_inputs": True}
     config = TorchYoloNasModelConfig(config_model)
     return TorchYoloNasModel(config)
 
@@ -95,18 +89,16 @@ class TorchYoloNasModel(fout.TorchImageModel):
 
     def _load_model(self, config):
         if config.model is not None:
-            self._model = config.model
+            model = config.model
         else:
-            if self._using_gpu:
-                self._model = super_gradients.training.models.get(
-                    config.yolo_nas_model, pretrained_weights=config.pretrained
-                ).cuda()
-            else:
-                self._model = super_gradients.training.models.get(
-                    config.yolo_nas_model, pretrained_weights=config.pretrained
-                )
+            model = super_gradients.training.models.get(
+                config.yolo_nas_model, pretrained_weights=config.pretrained
+            )
+
+        if self._using_gpu:
+            model = model.cuda()
 
-        return self._model
+        return model
 
     def _convert_bboxes(self, bboxes, w, h):
         tmp = np.copy(bboxes[:, 1])
@@ -135,6 +127,7 @@ def _generate_detections(self, p):
 
         if 0 in bboxes.shape:
             return fo.Detections(detections=[])
+
         bboxes = self._convert_bboxes(bboxes, width, height)
         labels = [class_names[l] for l in labels]
 
@@ -144,6 +137,9 @@ def _generate_detections(self, p):
         ]
         return fo.Detections(detections=detections)
 
-    def _predict_all(self, imgs):
-        preds = self._model.predict(imgs, conf=self.config.confidence_thresh)
+    def predict(self, img):
+        preds = self._model.predict(img, conf=self.config.confidence_thresh)
         return self._generate_detections(preds)
+
+    def predict_all(self, imgs):
+        return [self.predict(img) for img in imgs]