diff --git a/python/fastdeploy/vision/evaluation/detection.py b/python/fastdeploy/vision/evaluation/detection.py
index 98c6794fe..a13e0429e 100644
--- a/python/fastdeploy/vision/evaluation/detection.py
+++ b/python/fastdeploy/vision/evaluation/detection.py
@@ -23,7 +23,8 @@ def eval_detection(model,
                    ann_file,
                    conf_threshold=None,
                    nms_iou_threshold=None,
-                   plot=False):
+                   plot=False,
+                   batch_size=1):
     from .utils import CocoDetection
     from .utils import COCOMetric
     import cv2
@@ -54,6 +55,8 @@ def eval_detection(model,
     start_time = 0
     end_time = 0
     average_inference_time = 0
+    im_list = list()
+    im_id_list = list()
     for image_info, i in zip(all_image_info,
                              trange(
                                  image_num, desc="Inference Progress")):
@@ -61,19 +64,43 @@ def eval_detection(model,
             start_time = time.time()
         im = cv2.imread(image_info["image"])
         im_id = image_info["im_id"]
-        if conf_threshold is None and nms_iou_threshold is None:
-            result = model.predict(im.copy())
+        if batch_size == 1:
+            if conf_threshold is None and nms_iou_threshold is None:
+                result = model.predict(im.copy())
+            else:
+                result = model.predict(im, conf_threshold, nms_iou_threshold)
+            pred = {
+                'bbox': [[c] + [s] + b
+                         for b, s, c in zip(result.boxes, result.scores,
+                                            result.label_ids)],
+                'bbox_num': len(result.boxes),
+                'im_id': im_id
+            }
+            eval_metric.update(im_id, pred)
         else:
-            result = model.predict(im, conf_threshold, nms_iou_threshold)
-        pred = {
-            'bbox':
-            [[c] + [s] + b
-             for b, s, c in zip(result.boxes, result.scores, result.label_ids)
-             ],
-            'bbox_num': len(result.boxes),
-            'im_id': im_id
-        }
-        eval_metric.update(im_id, pred)
+            im_list.append(im)
+            im_id_list.append(im_id)
+            # If the batch_size is not satisfied, the remaining pictures are formed into a batch
+            if (i + 1) % batch_size != 0 and i != image_num - 1:
+                continue
+            if conf_threshold is None and nms_iou_threshold is None:
+                results = model.batch_predict(im_list)
+            else:
+                model.postprocessor.conf_threshold = conf_threshold
+                model.postprocessor.nms_threshold = nms_iou_threshold
+                results = model.batch_predict(im_list)
+            for k in range(len(im_list)):
+                pred = {
+                    'bbox': [[c] + [s] + b
+                             for b, s, c in zip(results[k].boxes, results[
+                                 k].scores, results[k].label_ids)],
+                    'bbox_num': len(results[k].boxes),
+                    'im_id': im_id_list[k]
+                }
+                eval_metric.update(im_id_list[k], pred)
+            im_list.clear()
+            im_id_list.clear()
+
         if i == image_num - 1:
             end_time = time.time()
     average_inference_time = round(