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Dogs vs Cats Image Classification using Python | CNN | Deep Learning Project Tutorial

Updated: May 15

Embark on the journey of image classification with Python! This tutorial explores CNN and deep learning techniques to classify images of dogs and cats. Learn to build accurate models that can distinguish between these furry friends, unlocking applications in pet recognition, animal monitoring, and more. Enhance your skills in computer vision, deep learning, and unleash the power of image classification. Join this comprehensive project tutorial to unravel the world of dogs vs cats image classification with python. #DogsVsCats #Python #CNN #DeepLearning #ImageClassification #ComputerVision

Dogs Vs Cats Image Classification
Dogs Vs Cats Image Classification

In this project tutorial we will use Convolutional Neural Network (CNN) for image feature extraction and visualize the results with plot graphs.

You can watch the video-based tutorial with step by step explanation down below.

Dataset Information

The training archive contains 25,000 images of dogs and cats. Train your algorithm on these files and predict the labels

(1 = dog, 0 = cat).

Download the dataset here

Environment: Google Colab

Download Dataset

We can download the dataset directly from the Microsoft page


--2021-05-06 16:04:20-- Resolving (, 2600:1417:8000:980::e59, 2600:1417:8000:9b2::e59 Connecting to (||:443... connected. HTTP request sent, awaiting response... 200 OK Length: 824894548 (787M) [application/octet-stream] Saving to: ‘’ kagglecatsanddogs_3 100%[===================>] 786.68M 187MB/s in 4.3s 2021-05-06 16:04:24 (183 MB/s) - ‘’ saved [824894548/824894548]

Unzip the Dataset

  • Run this code once and comment it

Import Modules

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
import os
import tqdm
import random
from keras.preprocessing.image import load_img
  • pandas - used to perform data manipulation and analysis

  • numpy - used to perform a wide variety of mathematical operations on arrays

  • matplotlib - used for data visualization and graphical plotting

  • os - used to handle files using system commands

  • tqdm - progress bar decorator for iterators

  • random - used for randomizing

  • load_img - used for loading the image as numpy array

  • warnings - to manipulate warnings details, filterwarnings('ignore') is to ignore the warnings thrown by the modules (gives clean results)

Create Dataframe for Input and Output

The Dogs vs Cats dataset may differ from where it was downloaded like folder structures or labels. You may create a dataframe to convert the input and output paths accordingly for easier processing.

input_path = []
label = []

for class_name in os.listdir("PetImages"):
    for path in os.listdir("PetImages/"+class_name):
        if class_name == 'Cat':
        input_path.append(os.path.join("PetImages", class_name, path))
print(input_path[0], label[0])

PetImages/Dog/4253.jpg 1

  • Adding the label to the images, one (1) for dogs and zero (0) for cats

  • Display the path of first image with corresponding label

Now we create the dataframe for processing

df = pd.DataFrame()
df['images'] = input_path
df['label'] = label
df = df.sample(frac=1).reset_index(drop=True)
Dog Vs Cat Dataset
Dog Vs Cat Dataset
  • Display of image paths with labels

  • Data was shuffled and the index was removed

We must remove any files in the data set that are not image data to avoid errors

for i in df['images']:
    if '.jpg' not in i:

PetImages/Cat/Thumbs.db PetImages/Dog/Thumbs.db

import PIL
l = []
for image in df['images']:
        img =

['PetImages/Cat/666.jpg', 'PetImages/Cat/Thumbs.db', 'PetImages/Dog/Thumbs.db', 'PetImages/Dog/11702.jpg']

  • List of non-image type files and corrupted images

# delete db files
df = df[df['images']!='PetImages/Dog/Thumbs.db']
df = df[df['images']!='PetImages/Cat/Thumbs.db']
df = df[df['images']!='PetImages/Cat/666.jpg']
df = df[df['images']!='PetImages/Dog/11702.jpg']


  • Dropping the corrupted files and non-image files from the dataset

Exploratory Data Analysis

Let us display a grid of images to know the content of the data

# to display grid of images
temp = df[df['label']==1]['images']
start = random.randint(0, len(temp))
files = temp[start:start+25]

for index, file in enumerate(files):
    plt.subplot(5,5, index+1)
    img = load_img(file)
    img = np.array(img)
Sample Dog Images
Sample Dog Images
Sample Dog Images
Sample Dog Images
Sample Dog Images
Sample Dog Images
  • Display of 25 random images of dogs

  • plt.axis('off') turns off both axis from the images

  • Files loaded and stored in an array

# to display grid of images
temp = df[df['label']==0]['images']
start = random.randint(0, len(temp))
files = temp[start:start+25]

for index, file in enumerate(files):
    plt.subplot(5,5, index+1)
    img = load_img(file)
    img = np.array(img)
Sample Cat Images
Sample Cat Images
Sample Cat Images
Sample Cat Images
Sample Cat Images
Sample Cat Images
  • Display of 25 random images of cats

  • Different saturation and qualities among the images

import seaborn as sns
Distribution of Labels
  • seaborn - built on top of matplotlib with similar functionalities

  • We can observe an equal distribution of both classes

Create Data Generator for the Images

Data Generators loads the data from the disk for reading and training the data directly, saving RAM space and avoiding possible overflow that might crash the system.

df['label'] = df['label'].astype('str')
Dog Vs Cat Dataset
  • Convert the data type of 'label' to string for easier processing

Let us split the input data

# input split
from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.2, random_state=42)

from keras.preprocessing.image import ImageDataGenerator
train_generator = ImageDataGenerator(
    rescale = 1./255,  # normalization of images
    rotation_range = 40, # augmention of images to avoid overfitting
    shear_range = 0.2,
    zoom_range = 0.2,
    horizontal_flip = True,
    fill_mode = 'nearest'

val_generator = ImageDataGenerator(rescale = 1./255)

train_iterator = train_generator.flow_from_dataframe(

val_iterator = val_generator.flow_from_dataframe(

Found 19998 validated image filenames belonging to 2 classes. Found 5000 validated image filenames belonging to 2 classes.

  • Image Generator rescale and normalizes the images by pixels between 0 and 1 for easier processing.

  • Augmentation applied to transform the images for more angles

  • batch_size=512 - amount of images to process per iteration

  • Assign batch size according to the hardware specs

  • class_mode='binary' indicates that there are 2 types of classes

Model Creation

from keras import Sequential
from keras.layers import Conv2D, MaxPool2D, Flatten, Dense

model = Sequential([
            Conv2D(16, (3,3), activation='relu', input_shape=(128,128,3)),
            Conv2D(32, (3,3), activation='relu'),
            Conv2D(64, (3,3), activation='relu'),
            Dense(512, activation='relu'),
            Dense(1, activation='sigmoid')
  • Dense - single dimension linear layer array

  • Conv2D - convolutional layer in 2 dimension

  • MaxPooling2D - function to get the maximum pixel value to the next layer

  • Flatten - convert 2D array into a 1D array

  • Use Dropout if augmentation was not applied on the images to avoid over fitting

  • activation='relu' - used commonly for image classification models

  • input_shape=(128,128,3) - Resolution size of the images in an RGB color scales. If in grayscale the third parameter is 1.

  • activation='sigmoid' - used for binary classification

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
CNN Model Summary
CNN Model Summary
  • model.compile() - compilation of the model

  • optimizer=’adam’ - automatically adjust the learning rate for the model over the no. of epochs

  • loss='binary_crossentropy' - loss function for binary outputs

history =, epochs=10, validation_data=val_iterator)

Epoch 1/10 40/40 [==============================] - 150s 4s/step - loss: 0.8679 - accuracy: 0.5187 - val_loss: 0.6399 - val_accuracy: 0.6238 Epoch 2/10 40/40 [==============================] - 147s 4s/step - loss: 0.6280 - accuracy: 0.6416 - val_loss: 0.5672 - val_accuracy: 0.7024 Epoch 3/10 40/40 [==============================] - 146s 4s/step - loss: 0.5737 - accuracy: 0.6980 - val_loss: 0.5493 - val_accuracy: 0.7148 Epoch 4/10 40/40 [==============================] - 146s 4s/step - loss: 0.5478 - accuracy: 0.7221 - val_loss: 0.5351 - val_accuracy: 0.7356 Epoch 5/10 40/40 [==============================] - 145s 4s/step - loss: 0.5276 - accuracy: 0.7338 - val_loss: 0.5104 - val_accuracy: 0.7494 Epoch 6/10 40/40 [==============================] - 144s 4s/step - loss: 0.5127 - accuracy: 0.7405 - val_loss: 0.4853 - val_accuracy: 0.7664 Epoch 7/10 40/40 [==============================] - 144s 4s/step - loss: 0.5059 - accuracy: 0.7544 - val_loss: 0.4586 - val_accuracy: 0.7868 Epoch 8/10 40/40 [==============================] - 143s 4s/step - loss: 0.4842 - accuracy: 0.7644 - val_loss: 0.5054 - val_accuracy: 0.7510 Epoch 9/10 40/40 [==============================] - 143s 4s/step - loss: 0.4971 - accuracy: 0.7530 - val_loss: 0.4647 - val_accuracy: 0.7894 Epoch 10/10 40/40 [==============================] - 142s 4s/step - loss: 0.4642 - accuracy: 0.7770 - val_loss: 0.4711 - val_accuracy: 0.7782

  • Assign the no. of epochs and batch size according to the hardware specifications

  • Training accuracy and validation accuracy increases each iteration

  • Training loss and validation loss decreases each iteration

Visualization of Results

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
epochs = range(len(acc))

plt.plot(epochs, acc, 'b', label='Training Accuracy')
plt.plot(epochs, val_acc, 'r', label='Validation Accuracy')
plt.title('Accuracy Graph')

loss = history.history['loss']
val_loss = history.history['val_loss']
plt.plot(epochs, loss, 'b', label='Training Loss')
plt.plot(epochs, val_loss, 'r', label='Validation Loss')
plt.title('Loss Graph')
Training & Validation Accuracy
Training & Validation Accuracy

Training & Validation Loss
Training & Validation Loss
  • Highest training accuracy was 78.6

  • Highest validation accuracy was 78.9

  • Lowest training loss was 46.42

  • Lowest validation loss was 46.47

Test with Real Image

image_path = "test.jpg" # path of the image
img = load_img(image_path, target_size=(128, 128))
img = np.array(img)
img = img / 255.0 # normalize the image
img = img.reshape(1, 128, 128, 3) # reshape for prediction
pred = model.predict(img)
if pred[0] > 0.5:
    label = 'Dog'
    label = 'Cat'


  • Apply the same preprocessing step from the previous cells and predict the probability from the model

  • If the probability is greater than 0.5, the label will be 'Dog' and for probability less than 0.5, the label will be 'Cat'

  • You can adjust the probability threshold and analyze the performance difference

Final Thoughts

  • Training the model by increasing the no. of epochs can give better and more accurate results.

  • Processing large amount of data can take a lot of time and system resource.

  • Basic deep learning model trained in a small neural network, adding new layers varies the results.

  • You may use other image classification models of your preference for comparison.

In this project tutorial, we have explored the Dogs vs Cats dataset as a classification deep learning project. This is a basic deep learning project to learn image classification analysis and visualize the results through different plots.

Get the project notebook from here

Thanks for reading the article!!!

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