upload v1667722548 model

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 test-image1.png filter=lfs diff=lfs merge=lfs -text
 test-image2.png filter=lfs diff=lfs merge=lfs -text
+test-image3.png filter=lfs diff=lfs merge=lfs -text
+test-image4.png filter=lfs diff=lfs merge=lfs -text
+test-image5.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -4,7 +4,7 @@ import tensorflow as tf
 from huggingface_hub import from_pretrained_keras
 from PIL import Image
 
-MODEL_CKPT = "chansung/segmentation-training-pipeline@v1667704347"
+MODEL_CKPT = "chansung/segmentation-training-pipeline@v1667722548"
 MODEL = from_pretrained_keras(MODEL_CKPT)
 
 RESOLTUION = 128
@@ -12,8 +12,9 @@ RESOLTUION = 128
 PETS_PALETTE = []
 with open(r"./palette.txt", "r") as fp:
     for line in fp:
-        tmp_list = list(map(int, line[:-1].strip("][").split(", ")))
-        PETS_PALETTE.append(tmp_list)
+        if "#" not in line:
+            tmp_list = list(map(int, line[:-1].strip("][").split(", ")))
+            PETS_PALETTE.append(tmp_list)
 
 
 def preprocess_input(image: Image) -> tf.Tensor:
@@ -86,7 +87,7 @@ demo = gr.Interface(
     allow_flagging="never",
     title=title,
     description=description,
-    examples=[["test-image1.png"], ["test-image2.png"]],
+    examples=[["test-image1.png"], ["test-image2.png"], ["test-image3.png"], ["test-image4.png"], ["test-image5.png"]],
 )
 
 demo.launch()

palette.txt

@@ -1,3 +1,6 @@
+# 0 for outer object
+# 1 for inner object
+# 2 for object border
 [0, 10, 146]
 [38, 0, 44]
 [255, 232, 0]