diff --git a/configs/cityscape.yaml b/configs/cityscape.yaml
index 0650d58a068e19c6cfe36f2d01c8e37dd5935045..f14234d77970e680cb39be46287f94ee5999c1a7 100644
--- a/configs/cityscape.yaml
+++ b/configs/cityscape.yaml
@@ -10,18 +10,6 @@ AUG:
     MIN_SCALE_FACTOR: 0.5  # for stepscaling
     SCALE_STEP_SIZE: 0.25  # for stepscaling
     MIRROR: True
-    RICH_CROP:
-        ENABLE: False
-        ASPECT_RATIO: 0.33
-        BLUR: True
-        BLUR_RATIO: 0.1
-        FLIP: True
-        FLIP_RATIO: 0.2
-        MAX_ROTATION: 15
-        MIN_AREA_RATIO: 0.5
-        BRIGHTNESS_JITTER_RATIO: 0.5
-        CONTRAST_JITTER_RATIO: 0.5
-        SATURATION_JITTER_RATIO: 0.5
 BATCH_SIZE: 4
 DATASET:
     DATA_DIR: "./dataset/cityscapes/"
@@ -44,8 +32,7 @@ TEST:
     TEST_MODEL: "snapshots/cityscape_v5/final/"
 TRAIN:
     MODEL_SAVE_DIR: "snapshots/cityscape_v7/"
-    PRETRAINED_MODEL: u"pretrain/deeplabv3plus_gn_init"
-    RESUME: False
+    PRETRAINED_MODEL_DIR: "pretrain/deeplabv3plus_gn_init"
     SNAPSHOT_EPOCH: 10
 SOLVER:
     LR: 0.001
diff --git a/configs/unet_pet.yaml b/configs/unet_pet.yaml
index 23bd68d5918fe89c3771cdc2e46871918b54e29c..2f3cc50e7e99ea7b8ff749d57f8319aa6b212a6f 100644
--- a/configs/unet_pet.yaml
+++ b/configs/unet_pet.yaml
@@ -12,18 +12,6 @@ AUG:
     MIN_SCALE_FACTOR: 0.75  # for stepscaling
     SCALE_STEP_SIZE: 0.25  # for stepscaling
     MIRROR: True
-    RICH_CROP:
-        ENABLE: False
-        ASPECT_RATIO: 0.33
-        BLUR: True
-        BLUR_RATIO: 0.1
-        FLIP: True
-        FLIP_RATIO: 0.2
-        MAX_ROTATION: 15
-        MIN_AREA_RATIO: 0.5
-        BRIGHTNESS_JITTER_RATIO: 0.5
-        CONTRAST_JITTER_RATIO: 0.5
-        SATURATION_JITTER_RATIO: 0.5
 BATCH_SIZE: 4
 DATASET:
     DATA_DIR: "./dataset/mini_pet/"
@@ -45,8 +33,7 @@ TEST:
     TEST_MODEL: "./test/saved_model/unet_pet/final/"
 TRAIN:
     MODEL_SAVE_DIR: "./test/saved_models/unet_pet/"
-    PRETRAINED_MODEL: "./test/models/unet_coco/"
-    RESUME: False
+    PRETRAINED_MODEL_DIR: "./test/models/unet_coco/"
     SNAPSHOT_EPOCH: 10
 SOLVER:
     NUM_EPOCHS: 500
diff --git a/docs/configs/train_group.md b/docs/configs/train_group.md
index 3c7c0fbb6efc04d232bbe3ba54275cb91bac699b..6c8a0d79c79af665d8c7bf54a2b7555aa024bb8d 100644
--- a/docs/configs/train_group.md
+++ b/docs/configs/train_group.md
@@ -11,7 +11,7 @@ TRAIN Group存放所有和训练相关的配置
 <br/>
 <br/>
 
-## `PRETRAINED_MODEL`
+## `PRETRAINED_MODEL_DIR`
 预训练模型路径
 
 ## 默认值
@@ -28,19 +28,15 @@ TRAIN Group存放所有和训练相关的配置
 <br/>
 <br/>
 
-## `RESUME`
-是否从预训练模型中恢复参数并继续训练
+## `RESUME_MODEL_DIR`
+从指定路径中恢复参数并继续训练
 
 ## 默认值
-False
+无
 
 ## 注意事项
 
-* 当该字段被置为True且`PRETRAINED_MODEL`不存在时，该选项不生效
-
-* 当该字段被置为True且`PRETRAINED_MODEL`存在时，PaddleSeg会恢复到上一次训练的最近一个epoch，并且恢复训练过程中的临时变量（如已经衰减过的学习率，Optimizer的动量数据等）
-
-* 当该字段被置为True且`PRETRAINED_MODEL`存在时，`PRETRAINED_MODEL`路径的最后一个目录必须为int数值或者字符串final，PaddleSeg会将int数值作为当前起始EPOCH继续训练，若目录为final，则不会继续训练。若目录不满足上述条件，PaddleSeg会抛出错误。
+* 当`RESUME_MODEL_DIR`存在时，PaddleSeg会恢复到上一次训练的最近一个epoch，并且恢复训练过程中的临时变量（如已经衰减过的学习率，Optimizer的动量数据等），`PRETRAINED_MODEL`路径的最后一个目录必须为int数值或者字符串final，PaddleSeg会将int数值作为当前起始EPOCH继续训练，若目录为final，则不会继续训练。若目录不满足上述条件，PaddleSeg会抛出错误。
 
 <br/>
 <br/>
@@ -57,4 +53,4 @@ False
 
 * 仅在GPU多卡训练时该开关有效（Windows不支持多卡训练，因此无需打开该开关）
 
-* GPU多卡训练时，建议开启该开关，可以提升模型的训练效果
\ No newline at end of file
+* GPU多卡训练时，建议开启该开关，可以提升模型的训练效果
diff --git a/docs/data_aug.md b/docs/data_aug.md
index 04a5ef3c6c3b37689fdace629f8973171904c389..92022bf54b3c791dcc9af820c54aad98e77e4428 100644
--- a/docs/data_aug.md
+++ b/docs/data_aug.md
@@ -35,9 +35,6 @@ rich crop是指对图像进行多种变换，保证在训练过程中数据的
 - blur
 图像加模糊，使用开关`AUG.RICH_CROP.BLUR`，为False时该项功能关闭。`AUG.RICH_CROP.BLUR_RATIO`控制加入模糊的概率。
 
-- flip
-图像上下翻转，使用开关`AUG.RICH_CROP.FLIP`，为False时该项功能关闭。`AUG.RICH_CROP.FLIP_RATIO`控制上下翻转的概率。
-
 - rotation
 图像旋转，`AUG.RICH_CROP.MAX_ROTATION`控制最大旋转角度。旋转产生的多余的区域的填充值为均值。
 
diff --git a/docs/usage.md b/docs/usage.md
index f9fc13bee08d9441b45664687a980c70b43bc9bd..7b06846c565281ede2dab7bc5d40eaa1eb607941 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -76,7 +76,7 @@ python pdseg/train.py --use_gpu \
                       --tb_log_dir train_log \
                       --cfg configs/unet_pet.yaml \
                       BATCH_SIZE 4 \
-                      TRAIN.PRETRAINED_MODEL pretrained_model/unet_bn_coco \
+                      TRAIN.PRETRAINED_MODEL_DIR pretrained_model/unet_bn_coco \
                       TRAIN.SYNC_BATCH_NORM True \
                       SOLVER.LR 5e-5
 ```
diff --git a/pdseg/reader.py b/pdseg/reader.py
index b4ebb4eba753b91aaa619c012611b7cab0614841..c839828cf99b89ceff62837cd6877a5659f80d06 100644
--- a/pdseg/reader.py
+++ b/pdseg/reader.py
@@ -261,17 +261,18 @@ class SegDataset(object):
                     SATURATION_JITTER_RATIO,
                     contrast_jitter_ratio=cfg.AUG.RICH_CROP.
                     CONTRAST_JITTER_RATIO)
-                if cfg.AUG.RICH_CROP.FLIP:
-                    if cfg.AUG.RICH_CROP.FLIP_RATIO <= 0:
-                        n = 0
-                    elif cfg.AUG.RICH_CROP.FLIP_RATIO >= 1:
-                        n = 1
-                    else:
-                        n = int(1.0 / cfg.AUG.RICH_CROP.FLIP_RATIO)
-                    if n > 0:
-                        if np.random.randint(0, n) == 0:
-                            img = img[::-1, :, :]
-                            grt = grt[::-1, :]
+
+            if cfg.AUG.FLIP:
+                if cfg.AUG.FLIP_RATIO <= 0:
+                    n = 0
+                elif cfg.AUG.FLIP_RATIO >= 1:
+                    n = 1
+                else:
+                    n = int(1.0 / cfg.AUG.FLIP_RATIO)
+                if n > 0:
+                    if np.random.randint(0, n) == 0:
+                        img = img[::-1, :, :]
+                        grt = grt[::-1, :]
 
             if cfg.AUG.MIRROR:
                 if np.random.randint(0, 2) == 1:
diff --git a/pdseg/train.py b/pdseg/train.py
index 3238701d0ec3832a098c26ae14ab3474fb76b873..295468ef14b87d8f4886fe0203d362bef3611a42 100644
--- a/pdseg/train.py
+++ b/pdseg/train.py
@@ -152,15 +152,15 @@ def load_checkpoint(exe, program):
     Load checkpoiont from pretrained model directory for resume training
     """
 
-    print('Resume model training from:', cfg.TRAIN.PRETRAINED_MODEL)
-    if not os.path.exists(cfg.TRAIN.PRETRAINED_MODEL):
+    print('Resume model training from:', cfg.TRAIN.RESUME_MODEL_DIR)
+    if not os.path.exists(cfg.TRAIN.RESUME_MODEL_DIR):
         raise ValueError("TRAIN.PRETRAIN_MODEL {} not exist!".format(
-            cfg.TRAIN.PRETRAINED_MODEL))
+            cfg.TRAIN.RESUME_MODEL_DIR))
 
     fluid.io.load_persistables(
-        exe, cfg.TRAIN.PRETRAINED_MODEL, main_program=program)
+        exe, cfg.TRAIN.RESUME_MODEL_DIR, main_program=program)
 
-    model_path = cfg.TRAIN.PRETRAINED_MODEL
+    model_path = cfg.TRAIN.RESUME_MODEL_DIR
     # Check is path ended by path spearator
     if model_path[-1] == os.sep:
         model_path = model_path[0:-1]
@@ -255,11 +255,11 @@ def train(cfg):
 
     # Resume training
     begin_epoch = cfg.SOLVER.BEGIN_EPOCH
-    if cfg.TRAIN.RESUME:
+    if cfg.TRAIN.RESUME_MODEL_DIR:
         begin_epoch = load_checkpoint(exe, train_prog)
     # Load pretrained model
-    elif os.path.exists(cfg.TRAIN.PRETRAINED_MODEL):
-        print('Pretrained model dir:', cfg.TRAIN.PRETRAINED_MODEL)
+    elif os.path.exists(cfg.TRAIN.PRETRAINED_MODEL_DIR):
+        print('Pretrained model dir:', cfg.TRAIN.PRETRAINED_MODEL_DIR)
         load_vars = []
         load_fail_vars = []
 
@@ -268,10 +268,10 @@ def train(cfg):
             Check whehter persitable variable shape is match with current network
             """
             var_exist = os.path.exists(
-                os.path.join(cfg.TRAIN.PRETRAINED_MODEL, var.name))
+                os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
             if var_exist:
                 var_shape = parse_shape_from_file(
-                    os.path.join(cfg.TRAIN.PRETRAINED_MODEL, var.name))
+                    os.path.join(cfg.TRAIN.PRETRAINED_MODEL_DIR, var.name))
                 return var_shape == shape
             return False
 
@@ -285,10 +285,10 @@ def train(cfg):
                     load_fail_vars.append(x)
         if cfg.MODEL.FP16:
             # If open FP16 training mode, load FP16 var separate
-            load_fp16_vars(exe, cfg.TRAIN.PRETRAINED_MODEL, train_prog)
+            load_fp16_vars(exe, cfg.TRAIN.PRETRAINED_MODEL_DIR, train_prog)
         else:
             fluid.io.load_vars(
-                exe, dirname=cfg.TRAIN.PRETRAINED_MODEL, vars=load_vars)
+                exe, dirname=cfg.TRAIN.PRETRAINED_MODEL_DIR, vars=load_vars)
         for var in load_vars:
             print("Parameter[{}] loaded sucessfully!".format(var.name))
         for var in load_fail_vars:
@@ -299,7 +299,7 @@ def train(cfg):
             len(load_vars) + len(load_fail_vars)))
     else:
         print('Pretrained model dir {} not exists, training from scratch...'.
-              format(cfg.TRAIN.PRETRAINED_MODEL))
+              format(cfg.TRAIN.PRETRAINED_MODEL_DIR))
 
     fetch_list = [avg_loss.name, lr.name]
     if args.debug:
diff --git a/pdseg/utils/config.py b/pdseg/utils/config.py
index d386721f46b09f2d306ad753d3d5d4b71639fbc1..1c1c56a6508988cf23fc382771593bd9e8cc52b4 100644
--- a/pdseg/utils/config.py
+++ b/pdseg/utils/config.py
@@ -69,6 +69,10 @@ cfg.DATASET.IGNORE_INDEX = 255
 ########################### 数据增强配置 ######################################
 # 图像镜像左右翻转
 cfg.AUG.MIRROR = True
+# 图像上下翻转开关，True/False
+cfg.AUG.FLIP = False
+# 图像启动上下翻转的概率，0-1
+cfg.AUG.FLIP_RATIO = 0.5
 # 图像resize的固定尺寸（宽，高），非负
 cfg.AUG.FIX_RESIZE_SIZE = tuple()
 # 图像resize的方式有三种：
@@ -107,18 +111,14 @@ cfg.AUG.RICH_CROP.CONTRAST_JITTER_RATIO = 0.5
 cfg.AUG.RICH_CROP.BLUR = False
 # 图像启动模糊百分比，0-1
 cfg.AUG.RICH_CROP.BLUR_RATIO = 0.1
-# 图像上下翻转开关，True/False
-cfg.AUG.RICH_CROP.FLIP = False
-# 图像启动上下翻转的概率，0-1
-cfg.AUG.RICH_CROP.FLIP_RATIO = 0.2
 
 ########################### 训练配置 ##########################################
 # 模型保存路径
 cfg.TRAIN.MODEL_SAVE_DIR = ''
 # 预训练模型路径
-cfg.TRAIN.PRETRAINED_MODEL = ''
+cfg.TRAIN.PRETRAINED_MODEL_DIR = ''
 # 是否resume，继续训练
-cfg.TRAIN.RESUME = False
+cfg.TRAIN.RESUME_MODEL_DIR = ''
 # 是否使用多卡间同步BatchNorm均值和方差
 cfg.TRAIN.SYNC_BATCH_NORM = False
 # 模型参数保存的epoch间隔数，可用来继续训练中断的模型
diff --git a/test/configs/deeplabv3p_xception65_cityscapes.yaml b/test/configs/deeplabv3p_xception65_cityscapes.yaml
index 612ac31bb304081a6c8900ff08d3ef61df62fbdf..349646f743f10c7970b248b30c258574c8478c68 100644
--- a/test/configs/deeplabv3p_xception65_cityscapes.yaml
+++ b/test/configs/deeplabv3p_xception65_cityscapes.yaml
@@ -10,18 +10,6 @@ AUG:
     MIN_SCALE_FACTOR: 0.5  # for stepscaling
     SCALE_STEP_SIZE: 0.25  # for stepscaling
     MIRROR: True
-    RICH_CROP:
-        ENABLE: False
-        ASPECT_RATIO: 0.33
-        BLUR: True
-        BLUR_RATIO: 0.1
-        FLIP: True
-        FLIP_RATIO: 0.2
-        MAX_ROTATION: 15
-        MIN_AREA_RATIO: 0.5
-        BRIGHTNESS_JITTER_RATIO: 0.5
-        CONTRAST_JITTER_RATIO: 0.5
-        SATURATION_JITTER_RATIO: 0.5
 BATCH_SIZE: 4
 DATASET:
     DATA_DIR: "./dataset/cityscapes/"
@@ -46,8 +34,7 @@ TEST:
     TEST_MODEL: "snapshots/cityscape_v5/final/"
 TRAIN:
     MODEL_SAVE_DIR: "snapshots/cityscape_v5/"
-    PRETRAINED_MODEL: "pretrain/deeplabv3plus_gn_init"
-    RESUME: False
+    PRETRAINED_MODEL_DIR: "pretrain/deeplabv3plus_gn_init"
     SNAPSHOT_EPOCH: 10
 SOLVER:
     LR: 0.001
diff --git a/test/configs/unet_pet.yaml b/test/configs/unet_pet.yaml
index e561e463bbdaddbe532d47a862c3e29095909f95..3a3cf65a09dfbff51e79ca65bd19c1c11fb75d64 100644
--- a/test/configs/unet_pet.yaml
+++ b/test/configs/unet_pet.yaml
@@ -12,18 +12,6 @@ AUG:
     MIN_SCALE_FACTOR: 0.75  # for stepscaling
     SCALE_STEP_SIZE: 0.25  # for stepscaling
     MIRROR: True
-    RICH_CROP:
-        ENABLE: False
-        ASPECT_RATIO: 0.33
-        BLUR: True
-        BLUR_RATIO: 0.1
-        FLIP: True
-        FLIP_RATIO: 0.2
-        MAX_ROTATION: 15
-        MIN_AREA_RATIO: 0.5
-        BRIGHTNESS_JITTER_RATIO: 0.5
-        CONTRAST_JITTER_RATIO: 0.5
-        SATURATION_JITTER_RATIO: 0.5
 BATCH_SIZE: 6
 DATASET:
     DATA_DIR: "./dataset/pet/"
@@ -45,8 +33,7 @@ TEST:
     TEST_MODEL: "./test/saved_model/unet_pet/final/"
 TRAIN:
     MODEL_SAVE_DIR: "./test/saved_models/unet_pet/"
-    PRETRAINED_MODEL: "./test/models/unet_coco/"
-    RESUME: False
+    PRETRAINED_MODEL_DIR: "./test/models/unet_coco/"
     SNAPSHOT_EPOCH: 10
 SOLVER:
     NUM_EPOCHS: 500
diff --git a/test/local_test_cityscapes.py b/test/local_test_cityscapes.py
index 6618695a60aae5f07230c546337b611d7c1cc78a..051faaa1b4e4c769a996b94b411b65815eb3a9e7 100644
--- a/test/local_test_cityscapes.py
+++ b/test/local_test_cityscapes.py
@@ -45,7 +45,8 @@ if __name__ == "__main__":
     saved_model = os.path.join(LOCAL_PATH, "saved_model", model_name)
 
     parser = argparse.ArgumentParser(description="PaddleSeg loacl test")
-    parser.add_argument("--devices",
+    parser.add_argument(
+        "--devices",
         dest="devices",
         help="GPU id of running. if more than one, use spacing to separate.",
         nargs="+",
@@ -75,7 +76,7 @@ if __name__ == "__main__":
     train(
         flags=["--cfg", cfg, "--use_gpu", "--log_steps", "10"],
         options=[
-            "SOLVER.NUM_EPOCHS", "1", "TRAIN.PRETRAINED_MODEL", test_model,
+            "SOLVER.NUM_EPOCHS", "1", "TRAIN.PRETRAINED_MODEL_DIR", test_model,
             "TRAIN.MODEL_SAVE_DIR", saved_model
         ],
         devices=devices)
diff --git a/test/local_test_pet.py b/test/local_test_pet.py
index 7d0cf58cd1235575fc960769d5142865993b5763..f043d16a5c7a9d7d45db8ce91864a8c5325876b9 100644
--- a/test/local_test_pet.py
+++ b/test/local_test_pet.py
@@ -46,7 +46,8 @@ if __name__ == "__main__":
     saved_model = os.path.join(LOCAL_PATH, "saved_model", model_name)
 
     parser = argparse.ArgumentParser(description="PaddleSeg loacl test")
-    parser.add_argument("--devices",
+    parser.add_argument(
+        "--devices",
         dest="devices",
         help="GPU id of running. if more than one, use spacing to separate.",
         nargs="+",
@@ -59,7 +60,7 @@ if __name__ == "__main__":
     train(
         flags=["--cfg", cfg, "--use_gpu", "--log_steps", "10"],
         options=[
-            "SOLVER.NUM_EPOCHS", "1", "TRAIN.PRETRAINED_MODEL", test_model,
+            "SOLVER.NUM_EPOCHS", "1", "TRAIN.PRETRAINED_MODEL_DIR", test_model,
             "TRAIN.MODEL_SAVE_DIR", saved_model, "DATASET.TRAIN_FILE_LIST",
             os.path.join(DATASET_PATH, "mini_pet", "file_list",
                          "train_list.txt"), "DATASET.VAL_FILE_LIST",
diff --git a/turtorial/finetune_deeplabv3plus.md b/turtorial/finetune_deeplabv3plus.md
index 082410e59fb11bb344dd381fef26f1e38eb3d0d5..02ce636a2f50f0387dea68a653b5443134a4edbc 100644
--- a/turtorial/finetune_deeplabv3plus.md
+++ b/turtorial/finetune_deeplabv3plus.md
@@ -67,7 +67,7 @@ MODEL:
     DEEPLAB:
         BACKBONE: "xception_65"
 TRAIN:
-    PRETRAINED_MODEL: "./pretrained_model/deeplabv3p_xception65_bn_pet/"
+    PRETRAINED_MODEL_DIR: "./pretrained_model/deeplabv3p_xception65_bn_pet/"
 
 
 # 其他配置
@@ -79,7 +79,6 @@ AUG:
 BATCH_SIZE: 4
 TRAIN:
     MODEL_SAVE_DIR: "./finetune/deeplabv3p_xception65_bn_pet/"
-    RESUME: False
     SNAPSHOT_EPOCH: 10
 TEST:
     TEST_MODEL: "./finetune/deeplabv3p_xception65_bn_pet/final"
diff --git a/turtorial/finetune_icnet.md b/turtorial/finetune_icnet.md
index 234e9d175769400916a06e314b6fe09471352f8c..3fbebb306860d73e0d04dd8db4020b9103cdd8cc 100644
--- a/turtorial/finetune_icnet.md
+++ b/turtorial/finetune_icnet.md
@@ -66,7 +66,7 @@ MODEL:
     DEFAULT_NORM_TYPE: "bn"
     MULTI_LOSS_WEIGHT: "[1.0, 0.4, 0.16]"
 TRAIN:
-    PRETRAINED_MODEL: "./pretrained_model/icnet_bn_cityscapes/"
+    PRETRAINED_MODEL_DIR: "./pretrained_model/icnet_bn_cityscapes/"
 
 
 # 其他配置
@@ -78,7 +78,6 @@ AUG:
 BATCH_SIZE: 4
 TRAIN:
     MODEL_SAVE_DIR: "./finetune/icnet_pet/"
-    RESUME: False
     SNAPSHOT_EPOCH: 10
 TEST:
     TEST_MODEL: "./finetune/icnet_pet/final"
@@ -118,4 +117,4 @@ python pdseg/eval.py --use_gpu --cfg ./configs/test_pet.yaml
 
 |预训练模型名称|BackBone|Norm|数据集|配置|
 |-|-|-|-|-|
-|icnet_bn_cityscapes|-|bn|Cityscapes|MODEL.MODEL_NAME: icnet <br> MODEL.DEFAULT_NORM_TYPE: bn|
+|icnet_bn_cityscapes|-|bn|Cityscapes|MODEL.MODEL_NAME: icnet <br> MODEL.DEFAULT_NORM_TYPE: bn <br> MULTI_LOSS_WEIGHT: [1.0, 0.4, 0.16]|
diff --git a/turtorial/finetune_unet.md b/turtorial/finetune_unet.md
index 0ae18101bc63c7cd0253ec5c8cbf7f3b74107d23..77a407bac09a8ad0866c66f585c49390205aa6d8 100644
--- a/turtorial/finetune_unet.md
+++ b/turtorial/finetune_unet.md
@@ -65,7 +65,7 @@ MODEL:
     MODEL_NAME: "unet"
     DEFAULT_NORM_TYPE: "bn"
 TRAIN:
-    PRETRAINED_MODEL: "./pretrained_model/unet_bn_coco/"
+    PRETRAINED_MODEL_DIR: "./pretrained_model/unet_bn_coco/"
 
 
 # 其他配置
@@ -77,7 +77,6 @@ AUG:
 BATCH_SIZE: 4
 TRAIN:
     MODEL_SAVE_DIR: "./finetune/unet_pet/"
-    RESUME: False
     SNAPSHOT_EPOCH: 10
 TEST:
     TEST_MODEL: "./finetune/unet_pet/final"