diff --git a/basicsr/archs/rrdbnet_arch.py b/basicsr/archs/rrdbnet_arch.py
index e1f31bcad..63d07080c 100644
--- a/basicsr/archs/rrdbnet_arch.py
+++ b/basicsr/archs/rrdbnet_arch.py
@@ -35,7 +35,7 @@ def forward(self, x):
         x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
         x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
         x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        # Emperically, we use 0.2 to scale the residual for better performance
+        # Empirically, we use 0.2 to scale the residual for better performance
         return x5 * 0.2 + x
 
 
@@ -59,7 +59,7 @@ def forward(self, x):
         out = self.rdb1(x)
         out = self.rdb2(out)
         out = self.rdb3(out)
-        # Emperically, we use 0.2 to scale the residual for better performance
+        # Empirically, we use 0.2 to scale the residual for better performance
         return out * 0.2 + x
 
 
diff --git a/basicsr/models/base_model.py b/basicsr/models/base_model.py
index f06f9ca2c..05c8d2e13 100644
--- a/basicsr/models/base_model.py
+++ b/basicsr/models/base_model.py
@@ -149,7 +149,7 @@ def print_network(self, net):
         logger.info(net_str)
 
     def _set_lr(self, lr_groups_l):
-        """Set learning rate for warmup.
+        """Set learning rate for warm-up.
 
         Args:
             lr_groups_l (list): List for lr_groups, each for an optimizer.
@@ -171,7 +171,7 @@ def update_learning_rate(self, current_iter, warmup_iter=-1):
 
         Args:
             current_iter (int): Current iteration.
-            warmup_iter (int)： Warmup iter numbers. -1 for no warmup.
+            warmup_iter (int)： Warm-up iter numbers. -1 for no warm-up.
                 Default： -1.
         """
         if current_iter > 1:
diff --git a/basicsr/utils/misc.py b/basicsr/utils/misc.py
index 728fef857..c8d4a1403 100644
--- a/basicsr/utils/misc.py
+++ b/basicsr/utils/misc.py
@@ -132,7 +132,7 @@ def sizeof_fmt(size, suffix='B'):
         suffix (str): Suffix. Default: 'B'.
 
     Return:
-        str: Formatted file siz.
+        str: Formatted file size.
     """
     for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']:
         if abs(size) < 1024.0:
diff --git a/docs/Config.md b/docs/Config.md
index 98aa2df76..d894407ed 100644
--- a/docs/Config.md
+++ b/docs/Config.md
@@ -19,7 +19,7 @@ Taking `001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb` as an example:
 - `DIV2K`: Training data is DIV2K
 - `1000k`: Total training iteration is 1000k
 - `B16G1`: Batch size is 16; one GPU is used for training
-- `wandb`: Use wandb logger; the training process has beed uploaded to wandb server
+- `wandb`: Use wandb logger; the training process has been uploaded to wandb server
 
 **Note**: If `debug` is in the experiment name, it will enter the debug mode. That is, the program will log and validate more intensively and will not use `tensorboard logger` and `wandb logger`.
 
diff --git a/docs/ModelZoo.md b/docs/ModelZoo.md
index 773baa5c1..87463aff5 100644
--- a/docs/ModelZoo.md
+++ b/docs/ModelZoo.md
@@ -18,7 +18,7 @@ You can put the downloaded models in the `experiments/pretrained_models` folder.
 
 **[Download official pre-trained models]** ([Google Drive](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing), [百度网盘](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g))
 
-You can use the scrip to download pre-trained models from Google Drive.
+You can use the script to download pre-trained models from Google Drive.
 
 ```python
 python scripts/download_pretrained_models.py ESRGAN
diff --git a/experiments/pretrained_models/README.md b/experiments/pretrained_models/README.md
index 9aceac1bd..d66240b97 100644
--- a/experiments/pretrained_models/README.md
+++ b/experiments/pretrained_models/README.md
@@ -2,7 +2,7 @@
 
 **[Download official pre-trained models](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing)**
 
-You can use the scrip to download pre-trained models from Google Drive.
+You can use the script to download pre-trained models from Google Drive.
 
 ```python
 python scripts/download_pretrained_models.py