WIP

examples/post_training_quantization/torch/ssd300_vgg16/main.py

@@ -274,13 +274,19 @@ def get_image():
 
 
 def main_detr():
-    from transformers import DetrImageProcessor, DetrForObjectDetection
+    from transformers import DetrImageProcessor, DetrForObjectDetection  # noqa
+    from transformers import AutoImageProcessor, AutoModelForObjectDetection, ConditionalDetrForObjectDetection  # noqa
+    from transformers import OwlViTProcessor, OwlViTForObjectDetection  # noqa
     import torch
 
     device = torch.device("cpu")
     # you can specify the revision tag if you don't want the timm dependency
-    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
-    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
+    # processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
+    # model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50", revision="no_timm")
+    processor = AutoImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50")
+    model = ConditionalDetrForObjectDetection.from_pretrained("microsoft/conditional-detr-resnet-50")
+    # processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
+    # model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32")
     model.eval()
 
     dataset_path = download_dataset()