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Created 3 years ago
10 Monkey Species Classification using Logistic Regression in PyTorch
Dataset can be downloaded from Kaggle: https://www.kaggle.com/slothkong/10-monkey-species
The dataset contains images of 10 monkey species which includes:
- n0 --> alouattapalliata
- n1 --> erythrocebuspatas
- n2 --> cacajaocalvus
- n3 --> macacafuscata
- n4 --> cebuellapygmea
- n5 --> cebuscapucinus
- n6 --> micoargentatus
- n7 --> saimirisciureus
- n8 --> aotusnigriceps
- n9 --> trachypithecusjohnii
There are two files in the dataset training and validation files.
Both training and validation folder contains 10 subfolders labelled as n0-n9, representing a species of monkey as labeled above.
Each images are at least 400 x 300 px [JPEG format]
- Total number of images in training folder: 1096
- Total number of images in validation folder: 272
# Import relevant libraries
import torch
import jovian
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import torch.nn.functional as F
from torchvision.datasets.utils import download_url
from torch.utils.data import DataLoader, TensorDataset, random_split
from PIL import Image
import glob
import cv2
project_name='10-Monkey-Species-Classification-Version-3' # will be used by jovian.commit
jovian.commit(project=project_name)
[jovian] Detected Colab notebook...
[jovian] Please enter your API key ( from https://jovian.ai/ ):
API KEY:
# Hyperparameters
batch_size = 64
learning_rate = 1e-3
jovian.reset()
jovian.log_hyperparams(batch_size=batch_size, learning_rate=learning_rate)
[jovian] Hyperparams logged.