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Nuts image Classification using FastAi library

I have chosen a problem of image classification of 7 types of nuts (-almonds, -hazelnuts, -macadamias, -peanuts, -pecans, -pistachios, -walnuts)

Lots of people are allergic to some particular kind of nuts, so this task can have a real-world application, if I will be able to achieve acceptable level of error (No more than 15%).

In this attempt#1 I used knowledge from the Course "Pytorch from zero to GANS", based on Jovian, but also I follow some advices from the course FastAi https://course.fast.ai/videos/?lesson=1

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
from fastai.vision import *