diff --git a/examples/.config/model_params_onnxrt.json b/examples/.config/model_params_onnxrt.json
index 5db34a114..085c7ef6c 100644
--- a/examples/.config/model_params_onnxrt.json
+++ b/examples/.config/model_params_onnxrt.json
@@ -55,6 +55,34 @@
         "input_model": "/tf_dataset2/models/onnx/Llama-2-7b-hf-with-past",
         "main_script": "main.py",
         "batch_size": 1
-      }
+      },
+      "bert_base_MRPC": {
+         "model_src_dir": "nlp/bert/quantization/ptq_static",
+         "dataset_location": "/tf_dataset/pytorch/glue_data/MRPC",
+         "input_model": "/tf_dataset2/models/onnx/bert_base_MRPC/bert.onnx",
+         "main_script": "main.py",
+         "batch_size": 8
+      },
+      "bert_base_MRPC_dynamic": {
+         "model_src_dir": "nlp/bert/quantization/ptq_dynamic",
+         "dataset_location": "/tf_dataset/pytorch/glue_data/MRPC",
+         "input_model": "/tf_dataset2/models/onnx/bert_base_MRPC/bert.onnx",
+         "main_script": "main.py",
+         "batch_size": 8
+      },
+      "resnet50-v1-12_qdq": {
+         "model_src_dir": "image_recognition/resnet50/quantization/ptq_static",
+         "dataset_location": "/tf_dataset2/datasets/imagenet/ImagenetRaw/ILSVRC2012_img_val",
+         "input_model": "/tf_dataset2/models/onnx/resnet50-v1-12/resnet50-v1-13.onnx",
+         "main_script": "main.py",
+         "batch_size": 1
+      },
+      "resnet50-v1-12": {
+         "model_src_dir": "image_recognition/resnet50/quantization/ptq_static",
+         "dataset_location": "/tf_dataset2/datasets/imagenet/ImagenetRaw/ILSVRC2012_img_val",
+         "input_model": "/tf_dataset2/models/onnx/resnet50-v1-12/resnet50-v1-12.onnx",
+         "main_script": "main.py",
+         "batch_size": 1
+      },
     }
   }
diff --git a/examples/image_recognition/resnet50/quantization/ptq_static/main.py b/examples/image_recognition/resnet50/quantization/ptq_static/main.py
index 4218769ca..232a12912 100644
--- a/examples/image_recognition/resnet50/quantization/ptq_static/main.py
+++ b/examples/image_recognition/resnet50/quantization/ptq_static/main.py
@@ -119,80 +119,6 @@ def result(self):
             return 0
         return self.num_correct / self.num_sample
 
-class Dataloader:
-    def __init__(self, dataset_location, image_list, batch_size):
-        self.batch_size = batch_size
-        self.image_list = []
-        self.label_list = []
-        with open(image_list, 'r') as f:
-            for s in f:
-                image_name, label = re.split(r"\s+", s.strip())
-                src = os.path.join(dataset_location, image_name)
-                if not os.path.exists(src):
-                    continue
-
-                self.image_list.append(src)
-                self.label_list.append(int(label))
-
-    def _preprpcess(self, src):
-        with Image.open(src) as image:
-            image = np.array(image.convert('RGB')).astype(np.float32)
-            image = image / 255.
-            image = cv2.resize(image, (256, 256), interpolation=cv2.INTER_LINEAR)
-
-            h, w = image.shape[0], image.shape[1]
-
-            y0 = (h - 224) // 2
-            x0 = (w - 224) // 2
-            image = image[y0:y0 + 224, x0:x0 + 224, :]
-            image = (image - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
-            image = image.transpose((2, 0, 1))
-        return image.astype('float32')
-
-    def __iter__(self):
-        return self._generate_dataloader()
-
-    def _generate_dataloader(self):
-        sampler = iter(range(0, len(self.image_list), 1))
-
-        def collate(batch):
-            """Puts each data field into a pd frame with outer dimension batch size"""
-            elem = batch[0]
-            if isinstance(elem, collections.abc.Mapping):
-                return {key: collate([d[key] for d in batch]) for key in elem}
-            elif isinstance(elem, collections.abc.Sequence):
-                batch = zip(*batch)
-                return [collate(samples) for samples in batch]
-            elif isinstance(elem, np.ndarray):
-                try:
-                    return np.stack(batch)
-                except:
-                    return batch
-            else:
-                return batch
-
-        def batch_sampler():
-            batch = []
-            for idx in sampler:
-                batch.append(idx)
-                if len(batch) == self.batch_size:
-                    yield batch
-                    batch = []
-            if len(batch) > 0:
-                yield batch
-
-        def fetcher(ids):
-            data = [self._preprpcess(self.image_list[idx]) for idx in ids]
-            label = [self.label_list[idx] for idx in ids]
-            return collate(data), label
-
-        for batched_indices in batch_sampler():
-            try:
-                data = fetcher(batched_indices)
-                yield data
-            except StopIteration:
-                return
-
 
 class DataReader(data_reader.CalibrationDataReader):
     def __init__(self, model_path, dataset_location, image_list, batch_size=1, calibration_sampling_size=-1):