#!conda install numpy pytorch torchvision cpuonly -c pytorch -y
#!pip install matplotlib --upgrade --quiet
#pip install pandas --upgrade --quiet
#!pip install seaborn --upgrade --quiet
!pip install jovian --upgrade --quiet

import os
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, random_split, DataLoader
from PIL import Image
import torchvision.models as models
import matplotlib as mlt
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm.notebook import tqdm
import torchvision.transforms as T
from sklearn.metrics import f1_score
import torch.nn.functional as F
import torch.nn as nn
from torchvision.utils import make_grid
%matplotlib inline

import jovian

project_name='course-project'

data_dir = '/kaggle/input/skin-cancer-mnist-ham10000'
print(os.listdir(data_dir))

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