Python AttributeError: 'Series' object has no attribute 'width'

前提・実現したいこと

こちらのコードをJupyter Notebookにコピーしながら実行していたところ、以下のようなエラーが出てしまいました。
DataFrameがSeriesとしてうまく認識されていないためこのようなエラーが出ているのだと思うのですが、どのようにコードを修正したらよいかわからずこちらに質問をさせていただきました。
大変お手数をお掛けしますが、解決方法についてご存じの方がいらっしゃいましたらご教示いただけますと幸いです。

発生している問題・エラーメッセージ

AttributeError                            Traceback (most recent call last)
<ipython-input-10-60593f14d96f> in <module>
----> 1 train_df['x_min'] = train_df.apply(lambda row: (row.x_min)/row.width, axis =1)
      2 train_df['y_min'] = train_df.apply(lambda row: (row.y_min)/row.height, axis =1)
      3 
      4 train_df['x_max'] = train_df.apply(lambda row: (row.x_max)/row.width, axis =1)
      5 train_df['y_max'] = train_df.apply(lambda row: (row.y_max)/row.height, axis =1)

~/home/user/libraries/Miniconda3/envs/test/lib/python3.6/site-packages/pandas/core/frame.py in apply(self, func, axis, raw, result_type, args, **kwds)
   7550             kwds=kwds,
   7551         )
-> 7552         return op.get_result()
   7553 
   7554     def applymap(self, func) -> "DataFrame":

~/home/user/libraries/Miniconda3/envs/test/lib/python3.6/site-packages/pandas/core/apply.py in get_result(self)
    183             return self.apply_raw()
    184 
--> 185         return self.apply_standard()
    186 
    187     def apply_empty_result(self):

~/home/user/libraries/Miniconda3/envs/test/lib/python3.6/site-packages/pandas/core/apply.py in apply_standard(self)
    274 
    275     def apply_standard(self):
--> 276         results, res_index = self.apply_series_generator()
    277 
    278         # wrap results

~/home/user/libraries/Miniconda3/envs/test/lib/python3.6/site-packages/pandas/core/apply.py in apply_series_generator(self)
    303                 for i, v in enumerate(series_gen):
    304                     # ignore SettingWithCopy here in case the user mutates
--> 305                     results[i] = self.f(v)
    306                     if isinstance(results[i], ABCSeries):
    307                         # If we have a view on v, we need to make a copy because

<ipython-input-10-60593f14d96f> in <lambda>(row)
----> 1 train_df['x_min'] = train_df.apply(lambda row: (row.x_min)/row.width, axis =1)
      2 train_df['y_min'] = train_df.apply(lambda row: (row.y_min)/row.height, axis =1)
      3 
      4 train_df['x_max'] = train_df.apply(lambda row: (row.x_max)/row.width, axis =1)
      5 train_df['y_max'] = train_df.apply(lambda row: (row.y_max)/row.height, axis =1)

~/home/user/libraries/Miniconda3/envs/test/lib/python3.6/site-packages/pandas/core/generic.py in __getattr__(self, name)
   5139             if self._info_axis._can_hold_identifiers_and_holds_name(name):
   5140                 return self[name]
-> 5141             return object.__getattribute__(self, name)
   5142 
   5143     def __setattr__(self, name: str, value) -> None:

AttributeError: 'Series' object has no attribute 'width'

該当のソースコード

!pip install --upgrade seaborn

import numpy as np, pandas as pd
from glob import glob
import shutil, os
import matplotlib.pyplot as plt
from sklearn.model_selection import GroupKFold
from tqdm.notebook import tqdm
import seaborn as sns

#dim = 512 #512, 256, 'original'　
fold = 4

train_df = pd.read_csv(f'./train.csv')
train_df.head()

train_df['image_path'] = f'./train_1_resized_jpeg/'+train_df.image_id+('.jpeg')#if dim!='original' else '.jpg')
train_df.head()

#train_df = train_df[train_df.class_id!=14].reset_index(drop = True)

train_df['x_min'] = train_df.apply(lambda row: (row.x_min)/row.width, axis =1)
train_df['y_min'] = train_df.apply(lambda row: (row.y_min)/row.height, axis =1)

train_df['x_max'] = train_df.apply(lambda row: (row.x_max)/row.width, axis =1)
train_df['y_max'] = train_df.apply(lambda row: (row.y_max)/row.height, axis =1)

train_df['x_mid'] = train_df.apply(lambda row: (row.x_max+row.x_min)/2, axis =1)
train_df['y_mid'] = train_df.apply(lambda row: (row.y_max+row.y_min)/2, axis =1)

train_df['w'] = train_df.apply(lambda row: (row.x_max-row.x_min), axis =1)
train_df['h'] = train_df.apply(lambda row: (row.y_max-row.y_min), axis =1)

train_df['area'] = train_df['w']*train_df['h']
train_df.head()