Build Your First Image Classification Model in Just 10 Minutes!

Pulkit Sharma 22 Jan, 2024 • 12 min read

13 minutes

Rating: 5 out of 5.


Build a deep learning image classification model in a few minutes without the hassle of lengthy training. No need for a powerful machine! I’ve heard this countless times from aspiring data scientists who shy away from building deep learning models on their own machines.

You don’t need to be working for Google or other big tech firms to work on deep learning datasets! It is entirely possible to build your own neural network from the ground up in a matter of minutes without needing to lease out Google’s servers.’s students designed a model on the Imagenet dataset in 18 minutes – and I will showcase something similar in this article.

Image Classification model

Deep learning is a vast field so we’ll narrow our focus a bit and take up the challenge of solving an Image Classification project. Additionally, we’ll be using a very simple deep learning architecture to achieve a pretty impressive accuracy score.

You can consider the Python code we’ll see in this article as a benchmark for building Image Classification models. Once you get a good grasp on the concept, go ahead and play around with the code, participate in competitions and climb up the leaderboard!

If you’re new to deep learning and are fascinated by the field of computer vision (who isn’t?!), do check out the ‘Computer Vision using Deep Learning‘ course. It’s a comprehensive introduction to this wonderful field and will set you up for what is inevitably going to a huge job market in the near future.

Project to apply Image Classification

Problem Statement

More than 25% of the entire revenue in E-Commerce is attributed to apparel & accessories. A major problem they face is categorizing these apparels from just the images especially when the categories provided by the brands are inconsistent. This poses an interesting computer vision problem that has caught the eyes of several deep learning researchers.

Fashion MNIST is a drop-in replacement for the very well known, machine learning hello world – MNIST dataset which can be checked out at ‘Identify the digits’ practice problem. Instead of digits, the images show a type of apparel e.g. T-shirt, trousers, bag, etc. The dataset used in this problem was created by Zalando Research.

Practice Now

What Is Image Classification?

Consider the below image:

You will have instantly recognized it – it’s a (swanky) car. Take a step back and analyze how you came to this conclusion – you were shown an image and you classified the class it belonged to (a car, in this instance). And that, in a nutshell, is what image classification is all about.

There are potentially n number of categories in which a given image can be classified. Manually checking and classifying images is a very tedious process. The task becomes near impossible when we’re faced with a massive number of images, say 10,000 or even 100,000. How useful would it be if we could automate this entire process and quickly label images per their corresponding class?

Self-driving cars are a great example to understand where image classification is used in the real-world. To enable autonomous driving, we can build an image classification model that recognizes various objects, such as vehicles, people, moving objects, etc. on the road. We’ll see a couple more use cases later in this article but there are plenty more applications around us. Use the comments section below the article to let me know what potential use cases you can come with up!

Now that we have a handle on our subject matter, let’s dive into how an image classification model is built, what are the prerequisites for it, and how it can be implemented in Python.

Setting Up the Structure of Our Image Data

Our data needs to be in a particular format in order to solve an image classification problem. We will see this in action in a couple of sections but just keep these pointers in mind till we get there.

You should have 2 folders, one for the train set and the other for the test set. In the training set, you will have a .csv file and an image folder:

  • The .csv file contains the names of all the training images and their corresponding true labels
  • The image folder has all the training images.

The .csv file in our test set is different from the one present in the training set. This test set .csv file contains the names of all the test images, but they do not have any corresponding labels. Can you guess why? Our model will be trained on the images present in the training set and the label predictions will happen on the testing set images

If your data is not in the format described above, you will need to convert it accordingly (otherwise the predictions will be awry and fairly useless).

Breaking Down the Process of Model Building

Before we deep dive into the Python code, let’s take a moment to understand how an image classification model is typically designed. We can divide this process broadly into 4 stages. Each stage requires a certain amount of time to execute:

  1. Loading and pre-processing Data – 30% time
  2. Defining Model architecture – 10% time
  3. Training the model – 50% time
  4. Estimation of performance – 10% time

Let me explain each of the above steps in a bit more detail. This section is crucial because not every model is built in the first go. You will need to go back after each iteration, fine-tune your steps, and run it again. Having a solid understanding of the underlying concepts will go a long way in accelerating the entire process.

Stage 1: Loading and pre-processing the data

Data is gold as far as deep learning models are concerned. Your image classification model has a far better chance of performing well if you have a good amount of images in the training set. Also, the shape of the data varies according to the architecture/framework that we use.

Hence, the critical data pre-processing step (the eternally important step in any project). I highly recommend going through the ‘Basics of Image Processing in Python’ to understand more about how pre-processing works with image data.

But we are not quite there yet. In order to see how our model performs on unseen data (and before exposing it to the test set), we need to create a validation set. This is done by partitioning the training set data.

In short, we train the model on the training data and validate it on the validation data. Once we are satisfied with the model’s performance on the validation set, we can use it for making predictions on the test data.

Time required for this step: We require around 2-3 minutes for this task.

Stage 2: Defining the model’s architecture

This is another crucial step in our deep learning model building process. We have to define how our model will look and that requires answering questions like:

  • How many convolutional layers do we want?
  • What should be the activation function for each layer?
  • How many hidden units should each layer have?

And many more. These are essentially the hyperparameters of the model which play a MASSIVE part in deciding how good the predictions will be.

How do we decide these values? Excellent question! A good idea is to pick these values based on existing research/studies. Another idea is to keep experimenting with the values until you find the best match but this can be quite a time consuming process.

Time required for this step: It should take around 1 minute to define the architecture of the model.

Stage 3: Training the model

For training the model, we require:

  • Training images and their corresponding true labels
  • Validation images and their corresponding true labels (we use these labels only to validate the model and not during the training phase)

We also define the number of epochs in this step. For starters, we will run the model for 10 epochs (you can change the number of epochs later).

Time required for this step: Since training requires the model to learn structures, we need around 5 minutes to go through this step.

And now time to make predictions!

Stage 4: Estimating the model’s performance

Finally, we load the test data (images) and go through the pre-processing step here as well. We then predict the classes for these images using the trained model.

Time required for this step: ~ 1 minute.

Setting Up the Problem Statement and Understanding the Data

We will be taking on a fascinating challenge to grasp image classification. The task involves constructing a model capable of categorizing a set of images based on apparel types like shirts, trousers, shoes, socks, etc. This particular problem is a common challenge for many e-commerce retailers, adding an extra layer of intrigue to this computer vision problem

This challenge is called ‘Identify the Apparels’ and is one of the practice problems we have on our DataHack platform. You will have to register and download the dataset from the above link.

Image classification model , the identify practice problem

We have a total of 70,000 images (28 x 28 dimension), out of which 60,000 are from the training set and 10,000 from the test one. The training images are pre-labelled according to the apparel type with 10 total classes. The test images are, of course, not labelled. The challenge is to identify the type of apparel present in all the test images.

We will build our model on Google Colab since it provides a free GPU to train our models.

Steps to Build Our Model

Time to fire up your Python skills and get your hands dirty. We are finally at the implementation part of our learning!

  1. Setting up Google Colab
  2. Importing Libraries
  3. Loading and Preprocessing Data – (3 mins)
  4. Creating a validation set
  5. Defining the model structure – (1 min)
  6. Training the model – (5 min)
  7. Making predictions – (1 min)

Let’s look at each step in detail.

Step 1: Setting up Google Colab

Since we’re importing our data from a Google Drive link, we’ll need to add a few lines of code in our Google Colab notebook. Create a new Python 3 notebook and write the following code blocks:

!pip install PyDrive

This will install PyDrive. Now we will import a few required libraries:

import os
from pydrive.auth import GoogleAuth
from import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials

Next, we will create a drive variable to access Google Drive:

gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)

To download the dataset, we will use the ID of the file uploaded on Google Drive:

download = drive.CreateFile({'id': '1BZOv422XJvxFUnGh-0xVeSvgFgqVY45q'})

Replace the ‘id’ in the above code with the ID of your file. Now we will download this file and unzip it:


You have to run these code blocks every time you start your notebook.

Step 2 : Import the libraries we’ll need during our model building phase.

import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import to_categorical
from keras.preprocessing import image
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from keras.utils import to_categorical
from tqdm import tqdm

Step 3: Recall the pre-processing steps we discussed earlier. We’ll be using them here after loading the data.

train = pd.read_csv('train.csv')

Next, we will read all the training images, store them in a list, and finally convert that list into a numpy array.

# We have grayscale images, so while loading the images we will keep grayscale=True, if you have RGB images, you should set grayscale as False
train_image = []
for i in tqdm(range(train.shape[0])):
    img = image.load_img('train/'+train['id'][i].astype('str')+'.png', target_size=(28,28,1), grayscale=True)
    img = image.img_to_array(img)
    img = img/255
X = np.array(train_image)

As it is a multi-class classification problem (10 classes), we will one-hot encode the target variable.

y = to_categorical(y)

Step 4: Creating a validation set from the training data.

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2)

Step 5: Define the model structure.

We will create a simple architecture with 2 convolutional layers, one dense hidden layer and an output layer.

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=(28,28,1)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))

Next, we will compile the model we’ve created.


Step 6: Training the model.

In this step, we will train the model on the training set images and validate it using, you guessed it, the validation set., y_train, epochs=10, validation_data=(X_test, y_test))

Step 7: Making predictions!

We’ll initially follow the steps we performed when dealing with the training data. Load the test images and predict their classes using the model.predict_classes() function.

download = drive.CreateFile({'id': '1KuyWGFEpj7Fr2DgBsW8qsWvjqEzfoJBY'})

Let’s import the test file:

test = pd.read_csv('test.csv')

Now, we will read and store all the test images:

test_image = []
for i in tqdm(range(test.shape[0])):
    img = image.load_img('test/'+test['id'][i].astype('str')+'.png', target_size=(28,28,1), grayscale=True)
    img = image.img_to_array(img)
    img = img/255
test = np.array(test_image)
# making predictions
prediction = model.predict_classes(test)

We will also create a submission file to upload on the DataHack platform page (to see how our results fare on the leaderboard).

download = drive.CreateFile({'id': '1z4QXy7WravpSj-S4Cs9Fk8ZNaX-qh5HF'})
# creating submission file
sample = pd.read_csv('sample_submission_I5njJSF.csv')
sample['label'] = prediction
sample.to_csv('sample_cnn.csv', header=True, index=False)

Download this sample_cnn.csv file and upload it on the contest page to generate your results and check your ranking on the leaderboard. This will give you a benchmark solution to get you started with any Image Classification problem!

You can try hyperparameter tuning and regularization techniques to improve your model’s performance further. I ecnourage you to check out this article to understand this fine-tuning step in much more detail – ‘A Comprehensive Tutorial to learn Convolutional Neural Networks from Scratch’.

How to build Image Classification Model?

image classification model

  1. Install Libraries:

    Install TensorFlow, NumPy, and Matplotlib.
    pip install tensorflow numpy matplotlib

  2. Prepare Data:
    Collect labeled images, split into training and testing sets.

  3. Build Model:
    Create a Convolutional Neural Network (CNN) using Keras.

    from tensorflow.keras import layers, models
    model = models.Sequential()
    … (add convolutional and dense layers)
    model.compile(optimizer=’adam’, loss=’categorical_crossentropy’, metrics=[‘accuracy’])

  4. Train Model:
    Train the model using your training data., train_labels, epochs=num_epochs, validation_data=(val_images, val_labels))

  5. Evaluate and Predict:
    Evaluate the model on the test set and make predictions.

    test_loss, test_acc = model.evaluate(test_images, test_labels)
    predictions = model.predict(test_images)

  6. Optional: Data Augmentation and Fine-Tuning:
    Use data augmentation for variety, and fine-tune as needed.

    Optional: Data Augmentation
    datagen = ImageDataGenerator(…), training_labels, batch_size=batch_size), epochs=num_epochs)
    Optional: Fine-Tuning
    Adjust hyperparameters or architecture based on performance.

New Practice Problem

Identify the digits, image classification model

Let’s test our learning on a different dataset. We’ll be cracking the ‘Identify the Digits’ practice problem in this section. Go ahead and download the dataset. Before you proceed further, try to solve this on your own. You already have the tools to solve it – you just need to apply them! Come back here to check your results or if you get stuck at some point.

In this challenge, we need to identify the digit in a given image. We have a total of 70,000 images – 49,000 labelled ones in the training set and the remaining 21,000 in the test set (the test images are unlabelled). We need to identify/predict the class of these unlabelled images.

Ready to begin? Awesome! Create a new Python 3 notebook and run the following code:

# Setting up Colab
!pip install PyDrive
import os
from pydrive.auth import GoogleAuth
from import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)
# Replace the id and filename in the below codes
download = drive.CreateFile({'id': '1ZCzHDAfwgLdQke_GNnHp_4OheRRtNPs-'})
# Importing libraries
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import to_categorical
from keras.preprocessing import image
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from keras.utils import to_categorical
from tqdm import tqdm
train = pd.read_csv('train.csv')
# Reading the training images
train_image = []
for i in tqdm(range(train.shape[0])):
    img = image.load_img('Images/train/'+train['filename'][i], target_size=(28,28,1), grayscale=True)
    img = image.img_to_array(img)
    img = img/255
X = np.array(train_image)
# Creating the target variable
y = to_categorical(y)
# Creating validation set
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2)
# Define the model structure
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=(28,28,1)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
# Compile the model
# Training the model, y_train, epochs=10, validation_data=(X_test, y_test))
download = drive.CreateFile({'id': '1zHJR6yiI06ao-UAh_LXZQRIOzBO3sNDq'})
test_file = pd.read_csv('Test_fCbTej3.csv')
test_image = []
for i in tqdm(range(test_file.shape[0])):
    img = image.load_img('Images/test/'+test_file['filename'][i], target_size=(28,28,1), grayscale=True)
    img = image.img_to_array(img)
    img = img/255
test = np.array(test_image)
prediction = model.predict_classes(test)
download = drive.CreateFile({'id': '1nRz5bD7ReGrdinpdFcHVIEyjqtPGPyHx'})
sample = pd.read_csv('Sample_Submission_lxuyBuB.csv')
sample['filename'] = test_file['filename']
sample['label'] = prediction
sample.to_csv('sample.csv', header=True, index=False)

Submit this file on the practice problem page to get a pretty decent accuracy number. It’s a good start but there’s always scope for improvement. Keep playing around with the hyperparameter values and see if you can improve on our basic model.


Who said image classification models required hours or days to train? My aim here was to showcase that you can come up with a pretty decent image classification model in double-quick time. You should pick up similar challenges and try to code them from your end as well. There’s nothing like learning by doing!

The top data scientists and analysts have these codes ready before a Hackathon even begins. They use these codes to make early submissions before diving into a detailed analysis. Once they have a benchmark solution, they start improving their model using different techniques.

Frequently Asked Questions

Q1. What is image classification?

A. Image classification is how a model classifies an image into a certain category based on pre-defined features or characteristics.

Q2. Which model is best for image classification?

A. The most popular image classification model is Convolutional Neural Network (CNN).

Q3. How do you create a model for image classification?

A. Follow the below steps to build your own image classification model:
Step 1: Set up Google Colab
Step 2: Import the required libraries
Step 3: Load and preprocess the data
Step 4: Create a validation set
Step 5: Define the model structure
Step 6: Train the model
Step 7: Start making predictions and test the model

Pulkit Sharma 22 Jan 2024

My research interests lies in the field of Machine Learning and Deep Learning. Possess an enthusiasm for learning new skills and technologies.

Frequently Asked Questions

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Responses From Readers


Pranov Shobhan Mishra
Pranov Shobhan Mishra 10 Jan, 2019

Hi Pulkit,This is a great article and timely as far as I am concerned. Off late, I have been trying to get some guidance on how to beat the computational power issue when building models on huge datasets. Using google as mentioned in your article is exactly the concept I was wanting to get some guidance on. And not just for Deep Learning models, this will be handy for other typical ML model exercises like RF, SVM and even text mining where after creating the DTM, data size explodes. I have faced difficulties in ensuring the model training completion because my laptop memory can be just as much. However I have been a R practitioner and not quite gone into Python so much as yet. Would it possible to give the exact same codes in R. If yes, it will be very helpful.

Charanteja 10 Jan, 2019

Hi, I have tried with the above mentioned code. I am getting an error for downloading the test data set. While downloading training data there was no error and model got trained well. However, while dowloading test data it is giving me an error "data not found". It may because of wrong file ID. Please mention how to find a correct file ID to download the testing data set?

Muhammad Nouman
Muhammad Nouman 10 Jan, 2019

Hi Pulkit,Thanks for the great article, it is very helpful.When I am trying to run this line: from google.colab import auth, I get this error: No module named 'google.colab'

B Srikar
B Srikar 10 Jan, 2019

Hi Great article.I have neve worked with google colab. You replied to Nouman above to run the codes in google colab. it. is there a turtorial for it or do yo have any instructions i can follow?thanks Srikar

Harish 10 Jan, 2019

Hi Pulkit, good article. I learnt a new thing today ie Google Colab. Can you please elaborate it further? I often find RAM issues on my laptop. Is Google Colab helpful here?

hervesiyou 11 Jan, 2019

hello, thank forr your course, i am a beginer but a lov dataminiing , i am blocked hereTo download the dataset, we will use the ID of the file uploaded on Google Drive:download = drive.CreateFile({'id': '1BZOv422XJvxFUnGh-0xVeSvgFgqVY45q'})which ID are you speaking about?? i hav not upload file on google drive, how can i do to continue thank

Apu 26 Jan, 2019

For those having trouble with uploading test file, download the test file from this link after signing up: the test file to your computerUpload it on your Google Drive and right click on the file > click share > click copy linkReplace ID in drive.createfile with shareable link and delete "" and "/view?usp=sharing"The part in the middle of the above two strings are your unique file ID

HARI KRISHNA 03 Feb, 2019

how to download the sample_cnn.csv file? can you mention command for that and process for that

Kinshuk 07 Mar, 2019

Hi I'm trying to run this code on my local machine but am getting the following error: FileNotFoundError: [Errno 2] No such file or directory: 'train/1.png'The following was the path used : train = pd.read_csv('E:/PGP_DS_2018/DataSets/Identify Apparels/train/train.csv')train_image =[] for i in tqdm(range(train.shape[0])): img = image.load_img('train/'+train['id'][i].astype('str') +'.png',target_size=(28,28,1),grayscale= True) img = image.img_to_array(img) img = img/255 train_image.append(img) X = np.array(train_image)Any help with the above will highly be appreciated!

diego 11 Mar, 2019

Hi Pulkit,Thanks for this extremely helpful guide. Can i check if i were to use images with color and, i have to set the grayscale=False right? I got an error like this when i set grayscale=False.ValueError: Error when checking input: expected conv2d_1_input to have shape (28, 28, 1) but got array with shape (28, 28, 3)If i were to change the target_size=(28,28,3), will it fix the problem? Also, where does the value 28 come from? Is it dependent on the size of the image? Cause i am not sure my image is of size dimension 28.

jawahar 17 Mar, 2019

"contest page to generate your results and check your ranking on the leaderboard" i cannot understand meaning of the above sentence. Kindly brief it out thanks in advance

Harshal Mali
Harshal Mali 20 Mar, 2019

Hi all, I want to make face detection model using edge detection what should i do step by step?

Rahul 02 Apr, 2019

When I try to get the test data with:"download = drive.CreateFile({'id': '1KuyWGFEpj7Fr2DgBsW8qsWvjqEzfoJBY'}) download.GetContentFile('') !unzip"I get: ApiRequestError:Does the file no longer exists ? or has the id/path been modified ?

Saikat Biswas
Saikat Biswas 04 Apr, 2019

Hi Pulkit,I am trying to use the test data code but getting an error every time I do that. It says FileNotFoundError: [Errno 2] No such file or directory: 'test/60001.png'I tried for the train data. It got trained well. But, the problem exists for the test file. All the train and test file are in the same folder. I am using local machine.Can you help please ?

Sowmya 05 Apr, 2019

I am getting this error when I try it with my own set of images(60 training data) and no. of classes=3. How do I fix this? I tried to change the output layer's value to 4 because there are 3 classes but that just freezes the system. Error: Error when checking target: expected dense_2 to have shape (10,) but got array with shape (4,)

Ahmed Mabrouk
Ahmed Mabrouk 16 Apr, 2019

Hello , First of all thanks a lot for your effort for that amazing instructions i’m asking where can i show your training data (id = 1BZOv422XJvxFUnGh-0xVeSvgFgqVY45q') after i have downloaded it ?

Rodolfo Peña
Rodolfo Peña 18 Apr, 2019

hi,I am gettimg a No module named colab error when I run the second block of code. Am I supposed to import sometimg else to be able acces colab?

Rodolfo 19 Apr, 2019

Hi Pulkit,I have trained the neural net successfully yet, when I run: download = drive.CreateFile({'id': '1zHJR6yiI06ao-UAh_LXZQRIOzBO3sNDq'}) download.GetContentFile('Test_fCbTej3.csv') I get a file not found error. I can see other people have had the same problem and you have explained it. but please explain to me how to fix this as if I was 6 years old, although I am 71 but I am a bit slow in comprehending.Thank you for your patience.

Anjelou 22 Apr, 2019

Hi,Will thhis work on Windows powered computer? or just in Ubuntu? I'm using Windows.Thank you.

Mukund Choudhary
Mukund Choudhary 25 Apr, 2019

Greetings Pulkit, really appreciate the article. Just one doubt,For the submission file, I'm getting a file not found error. Am I supposed to give a different ID here too, but I'm creating a new file for submission so which ID am I supposed to use here?

srinivasan krishnan
srinivasan krishnan 06 May, 2019

Hi,Can you share some of the images (like .jpg, img, JPEG 2000 Exif. ... TIFF. ... GIF. ... BMP. ... PNG. ... )can be used in classification models. Can I use this images from my desktop. Or its should be only from cloud?

Neha_02 12 May, 2019

Hii Pulkit Thanks for sharing the valuable information about Machine learning!!I am getting this error message when I tried to run the code in colabmodel.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy']) ^ SyntaxError: invalid syntax

Neha_02 12 May, 2019

Hii Pulkit! Thanks for sharing this valuable info on image classificationI encountered an error while compiling the code in colab Can u please help to resolve it?The errror is: File "", line 63 model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy']) ^ SyntaxError: invalid syntax

Shivani Parab
Shivani Parab 04 Jun, 2019

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2)Getting memory error at this step. I tried changing random_state values to 21,10,1,0, etc. but still getting the error

sree 07 Jun, 2019

Hi Pulkit,i am facing problem with the following line img = image.load_img('test1/'+str(index)+'.jpg', target_size=(150,150,1), grayscale=True)OUTPUT:FileNotFoundError Traceback (most recent call last) in () 4 import numpy as np 5 for index in range(10): ----> 6 img = image.load_img('test1/'+str(index)+'.jpg', target_size=(150,150,1), grayscale=True) 7 im2arr = np.array(img) 8 im2arr = im2arr.reshape(1,150,150,1)1 frames /usr/local/lib/python3.6/dist-packages/PIL/ in open(fp, mode) 2528 2529 if filename: -> 2530 fp =, "rb") 2531 exclusive_fp = True 2532FileNotFoundError: [Errno 2] No such file or directory: 'test1/0.jpg'i uploaded the test file also and it is able to unzip it, but i am not able to load the image with that path. please help me out this problem.

chad 11 Jun, 2019

If I have a labeled test set, how can I measure my prediction performance?

sabyasachi kabiraj
sabyasachi kabiraj 15 Jun, 2019

i am working on image classification using ANN but as a beginner i dont have any knowledge about this machine learning. Can you help me by making tutorials or step by step notes? so that i can classify my image according my classes

nabilasirtahir 20 Jun, 2019

Hi,I've try your code and find this article very helpful. When I trained 2 classes, it can predict those two classes, However is that possible if i feed a 'never seen' image, it give me a unknown class instead of the two trained class.

Abhijeet 24 Jun, 2019

facing problem in downloading data set to it to google drive. It would be really helpful if you can share the link of the required data . actually I am a beginner

André Novaes
André Novaes 02 Jul, 2019

Hi! I got a job thanks to this tutorial! Thank you very much!Do you have books published?

Sina 04 Jul, 2019

Hi, Where is the prediction csv file stored? I cannot really find the final file to submit.Thanks,

Steve 04 Jul, 2019

Some of the code generates deprecation warnings. I can deal with it, but it would be nice to make the tutorial current.

Meet Joshi
Meet Joshi 07 Jul, 2019

Can you please share the download links of train and test datasets? They are no longer available on website after signup.

Sakti Subramanian
Sakti Subramanian 10 Jul, 2019

I'm having trouble with the CSV Line, or train = pd.read_csv('train.csv'). I had watched other videos for image classification, that used datasets WITH labeled images in categories, but WITHOUT numerical data. How do I go about creating an image classification system now?

jeyanthi 18 Aug, 2019

Hello PULKIT SHARMA, Wonderful article, i have one doubt. When i run this code in google colab i got this error. Help me to resolve Code: download.GetContentFile('/content/drive/My Drive/Colab Notebooks/ipynb/') !unzip ''Error: FileNotDownloadableError Traceback (most recent call last) in () ----> 1 download.GetContentFile('/content/drive/My Drive/Colab Notebooks/ipynb/') 2 get_ipython().system("unzip ''")2 frames /usr/local/lib/python3.6/dist-packages/pydrive/ in FetchContent(self, mimetype, remove_bom) 263 else: 264 raise FileNotDownloadableError( --> 265 'No downloadLink/exportLinks for mimetype found in metadata') 266 267 if mimetype == 'text/plain' and remove_bom:FileNotDownloadableError: No downloadLink/exportLinks for mimetype found in metadata Thanks in advance

Ajay Tiwari
Ajay Tiwari 20 Aug, 2019

Hi Pulkit,I wanted to use annotated labels like x y coordinates (x1,y1,x2,y2) for my region of interest along with class labels. Can I do this following the discussed approach?Thanks,Ajay

Evgeny 23 Aug, 2019

Hi Pulkit, Great article, thanks. You mention that this code uses GPU provided by Colab Notebook. If I want to modify this code to run on premises - what is minimum GPU specs recommended? E.g. If I run it on a laptop - should it be a gaming laptop?

vinothkumar 23 Aug, 2019

i have doubt in the last step of creating the sample submission file. can you please tell me how to create it in the drive.

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