diff --git a/bin/train.sh b/bin/train.sh
index 137c911..4172765 100755
--- a/bin/train.sh
+++ b/bin/train.sh
@@ -1,10 +1,10 @@
 #!/usr/bin/env bash
 
-export CUDA_VISIBLE_DEVICES=2,3
+export CUDA_VISIBLE_DEVICES=0,1,2,3
 RUN_CONFIG=config.yml
 
 
-LOGDIR=/raid/bac/kaggle/logs/recursion_cell/test/c1234_s1_affine_warmup/se_resnext50_32x4d/
+LOGDIR=./bin/30_epochs/
 catalyst-dl run \
     --config=./configs/${RUN_CONFIG} \
     --logdir=$LOGDIR \
diff --git a/configs/config.yml b/configs/config.yml
index 9e1fe8a..a6a7968 100644
--- a/configs/config.yml
+++ b/configs/config.yml
@@ -24,16 +24,17 @@ stages:
 
   data_params:
     batch_size: 64
-    num_workers: 8
+    num_workers: 16
     drop_last: False
+    # drop_last: True
 
     image_size: &image_size 512
     train_csv: "./csv/train_0.csv"
     valid_csv: "./csv/valid_0.csv"
-    root: "/raid/data/kaggle/recursion-cellular-image-classification/"
+    root: "./data/"
     sites: [1]
     channels: [1, 2, 3, 4]
-
+##################################################
   stage0:
 
     optimizer_params:
@@ -63,7 +64,7 @@ stages:
         reduce_metric: *reduce_metric
       saver:
         callback: CheckpointCallback
-
+##########################################################
   stage1:
 
     optimizer_params:
@@ -72,12 +73,48 @@ stages:
 
     scheduler_params:
       scheduler: OneCycleLR
-      num_steps: &num_epochs 30
+      num_steps: 50
       lr_range: [0.0005, 0.00001]
+      # lr_range: [0.0015, 0.00003]
       warmup_steps: 5
       momentum_range: [0.85, 0.95]
 
     state_params:
-      num_epochs: *num_epochs
-
-    callbacks_params: *callback_params
\ No newline at end of file
+      num_epochs: 50
+
+    callbacks_params: *callback_params
+######################################################
+  # stage2:
+
+  #   optimizer_params:
+  #     optimizer: Nadam
+  #     lr: 0.00001
+
+  #   scheduler_params:
+  #     scheduler: ReduceLROnPlateau
+  #     patience: 2
+  #     # num_steps: 30
+  #     # lr_range: [0.0007, 0.00001]
+  #     # warmup_steps: 0
+  #     # momentum_range: [0.85, 0.95]
+
+  #   state_params:
+  #     num_epochs: 10
+
+  #   criterion_params:
+  #       criterion: LabelSmoothingCrossEntropy
+
+  #   callbacks_params:
+  #     loss:
+  #       callback: LabelSmoothCriterionCallback
+  #     optimizer:
+  #       callback: OptimizerCallback
+  #       accumulation_steps: 2
+  #     accuracy:
+  #       callback: AccuracyCallback
+  #       accuracy_args: [1]
+  #     scheduler:
+  #       callback: SchedulerCallback
+  #       reduce_metric: *reduce_metric
+  #     saver:
+  #       callback: CheckpointCallback
diff --git a/src/augmentation.py b/src/augmentation.py
index af456c6..3bd7db1 100644
--- a/src/augmentation.py
+++ b/src/augmentation.py
@@ -4,22 +4,24 @@
 
 
 def train_aug(image_size=224):
-    policies = './csv/best_policy.data'
-    with open(policies, 'r') as fid:
-        policies = eval(fid.read())
-        policies = itertools.chain.from_iterable(policies)
-
-    aug_list = []
-    for policy in policies:
-        op_1, params_1 = policy[0]
-        op_2, params_2 = policy[1]
-        aug = Compose([
-            globals().get(op_1)(**params_1),
-            globals().get(op_2)(**params_2),
-        ])
-        aug_list.append(aug)
+    # policies = './csv/best_policy.data'
+    # with open(policies, 'r') as fid:
+    #     policies = eval(fid.read())
+    #     policies = itertools.chain.from_iterable(policies)
+
+    # aug_list = []
+    # for policy in policies:
+    #     op_1, params_1 = policy[0]
+    #     op_2, params_2 = policy[1]
+    #     aug = Compose([
+    #         globals().get(op_1)(**params_1),
+    #         globals().get(op_2)(**params_2),
+    #     ])
+    #     aug_list.append(aug)
 
     return Compose([
+        ChannelDropout(),
+        # CLAHE(),
         RandomRotate90(),
         Flip(),
         Transpose(),
diff --git a/src/make_submission.py b/src/make_submission.py
index d2bc50a..f1c779f 100644
--- a/src/make_submission.py
+++ b/src/make_submission.py
@@ -34,14 +34,14 @@ def predict(model, loader):
 
 
 def predict_all():
-    test_csv = '/raid/data/kaggle/recursion-cellular-image-classification/test.csv'
+    test_csv = './data/test.csv'
     # test_csv = './csv/valid_0.csv'
 
-    for fold in [0, 1, 2, 3]:
+    for fold in [0,1,2,3,4]:
         model_name = 'se_resnext50_32x4d'
 
-        log_dir = f"/raid/bac/kaggle/logs/recursion_cell/search_channels/fold_{fold}/[1,2,3,4]/se_resnext50_32x4d/"
-        root = "/raid/data/kaggle/recursion-cellular-image-classification/"
+        log_dir = f"./bin/log/checkpoints/"
+        root = "./data/"
         sites = [1]
         channels = [1,2,3,4]
 
@@ -73,7 +73,7 @@ def predict_all():
                 dataset=dataset,
                 batch_size=128,
                 shuffle=False,
-                num_workers=4,
+                num_workers=12,
             )
 
             pred = predict(model, loader)
@@ -90,14 +90,14 @@ def predict_all():
 
 
 def predict_one():
-    test_csv = '/raid/data/kaggle/recursion-cellular-image-classification/test.csv'
+    test_csv = './data/test.csv'
     # test_csv = './csv/valid_0.csv'
 
     model_name = 'se_resnext50_32x4d'
-    experiment = "c1234_s1_affine_warmup"
+    experiment = "dropout_channel"
 
-    log_dir = f"/raid/bac/kaggle/logs/recursion_cell/test/{experiment}/{model_name}/"
-    root = "/raid/data/kaggle/recursion-cellular-image-classification/"
+    log_dir = f"./bin/log/"
+    root = "./data/"
     sites = [1]
     channels = [1,2,3,4]
 
diff --git a/src/models/senet.py b/src/models/senet.py
index ddf156b..b9dc777 100644
--- a/src/models/senet.py
+++ b/src/models/senet.py
@@ -36,6 +36,7 @@ def cell_senet(model_name='se_resnext50_32x4d', num_classes=1108, n_channels=6):
         pretrained=True,
         # pool=GlobalConcatPool2d(),
         # classifier_factory=make_classifier
+        dropout_p=0.3
     )
     # print(model)
     conv1 = model._features[0].conv1