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requirements.txt
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requirements.txt
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# Necessary imports required to run our code-
import cv2
import numpy as np
import concurrent.futures
from tqdm import tqdm
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import skew, kurtosis
from skimage.feature import hog
from skimage import exposure
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import tensorflow as tf
from tensorflow import keras
from keras import Sequential
from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, BatchNormalization, Dropout
import torch
import torch.nn as nn #imports the neural network module which contains the nn superclass
import torch.optim as optim #imports the optimization algorithms such as gradient descent, adam etc
import torch.nn.functional as F #has all the parameter-less functions, imports the activation functions(relu etc), but those can also be found in the nn package
from torch.utils.data import DataLoader #this provides a dataset class for data representation and a dataloader for iterating over the data among other things.
import torchvision.datasets as datasets #pytorch comes with datasets which can be imported through this
import torchvision.transforms as transforms #has methods to perform data augmentation operations such as cropping, resizing, normalization etc.