diff --git a/datasets.py b/datasets.py
index df0ee01..57e810a 100644
--- a/datasets.py
+++ b/datasets.py
@@ -59,6 +59,7 @@ class CustomDataset(Dataset):
         target["iscrowd"] = iscrowd
         image_id = torch.tensor([idx])
         target["image_id"] = image_id
+        target["image_name"] = image_name
 
         return img_tensor, target
 
diff --git a/inference.py b/inference.py
index 36953cd..2cbed5d 100644
--- a/inference.py
+++ b/inference.py
@@ -14,12 +14,11 @@ from tqdm.auto import tqdm
 import matplotlib.pyplot as plt
 from matplotlib.patches import Rectangle
 
-def show_sample(img_tensor, outputs, detection_threshold):
+def plot_inference(img_tensor, output, target, detection_threshold):
 
-    fig, ax = plt.subplots()
+    fig, ax = plt.subplots(figsize=(7, 7), num=target['image_name'])
     ax.imshow(img_tensor.squeeze().permute(1, 2, 0), aspect='auto')
-    for (x0, y0, x1, y1), l, score in zip(outputs[0]['boxes'].cpu(), outputs[0]['labels'].cpu(), outputs[0]['scores'].cpu()):
-
+    for (x0, y0, x1, y1), l, score in zip(output[0]['boxes'].cpu(), output[0]['labels'].cpu(), output[0]['scores'].cpu()):
         if score < detection_threshold:
             continue
     #     print(x0, y0, x1, y1, l)
@@ -28,9 +27,18 @@ def show_sample(img_tensor, outputs, detection_threshold):
             Rectangle((x0, y0),
                       (x1 - x0),
                       (y1 - y0),
-                      fill=False, color="white", linewidth=2, zorder=10)
+                      fill=False, color="tab:green", linewidth=2, zorder=10)
         )
+    for (x0, y0, x1, y1), l in zip(target['boxes'], target['labels']):
+        ax.add_patch(
+            Rectangle((x0, y0),
+                      (x1 - x0),
+                      (y1 - y0),
+                      fill=False, color="white", linewidth=2, zorder=9)
+        )
+
     plt.show()
+    embed()
 
 def infere_model(test_loader, model, detection_th=0.8):
 
@@ -44,7 +52,8 @@ def infere_model(test_loader, model, detection_th=0.8):
         with torch.inference_mode():
             outputs = model(images)
 
-        embed()
+        for image, output, target in zip(images, outputs, targets):
+            plot_inference(image, output, target, detection_th)
 
 
 if __name__ == '__main__':