The document describes a machine learning project to classify images of dogs and cats using different models. The team used random forest initially, achieving 58% accuracy. They then used a neural network and CNN, reaching 60% and 79.45% accuracy, respectively. Transfer learning with pre-trained VGG19 and ResNet50 models significantly improved accuracy to 90.61% and 95.8%. Finally, fine-tuning pretrained ResNet50 by tweaking the last 15 convolutional layers achieved the highest accuracy of 95.55%

1. MACHINE LEARNING
PROJECT
ECKOVATION
TOPIC:- DOGS VS CATS
CLASSIFICATION
TEAM NAME : “ERROR_404”
SUBMITTED BY:-
HARSH JAIN (01151203116)
PARV BHARTI(02151203116)
SHANKAR(028512031156)
VISHAL DUBEY(O3451203116)
SUDHANSHU
GOEL(03751203116)

2. AIM
AIM :-
• Our goal was to classify images of dogs and cats from the
given data of 25000 images.
• In our examples we will use two sets of pictures, which we
got from Kaggle 12500 cats and 12500 dogs.

3. Approach
• We Started with Random Forest
• Then we applied Deep Neural network
• We then applied Convolution Neural Network
• Then We Applied CNN with pre-trained model (Transfer
Learning)
• Last but not least we applied fine tuned pre-trained model

4. Random Forest
• The Accuracy when applying Random Forest was 58%.

5. Neural network
• In Neural network the input feature is represented by nodes in input
layers and output by nodes in output layer.

6. What happens in deep neural network?
• Each pixel of image is represented by a node in input layer and are
multiplied by an appropriate weight and then a activation function is
applied on the result which act as the input of next layer and this
process is carried out till the output layer.
• The activation function make output non-linear and neural network
can be applied to higher order logics.
• The error is computed by comparing actual output and output of
neural network.

7. How Weights are found?
• First batches of images are fed into the network with random
weights then output with those weights are calculated and error is
determined. Then the error is back-propagated and weights are
adjusted. This process is continued till error is reduced to minimal.

8. Our Experience with neural network
• When we applied neural network we got an accuracy of 60% which
is not par and is almost identical to Random forest

9. CONVOLUTIONAL NEURAL
NETWORK (CNN)
• Convolutional Neural Networks are a form of Feedforward Neural Networks. Given below is a schema of a
typical CNN. The first part consists of Convolutional and max-pooling layers which act as the feature extractor.
The second part consists of the fully connected layer which performs non-linear transformations of the
extracted features and acts as the classifier.
•

10. HOW CNN IMAGE CLASSIFIER
WORK
• The convolutional layer can be thought of as the eyes of the CNN. The
neurons in this layer look for specific features. If they find the features
they are looking for, they produce a high activation.
• Convolution can be thought of as a weighted sum between two signals
( in terms of signal processing jargon ) or functions ( in terms of
mathematics ).

11. • How convolution at a place is calculated

12. CNNs learn Hierarchical features

13. Our Experience with CNN
• We got an accuracy of 79.45

14. What Next?
• Since we have very less data compared to what is required in deep
neural networks. Even if we get the data, it takes a large amount of
time to train the network ( hundreds of hours ).
• We will use pre-trained models. The models have been trained on
millions of images and for hundreds of hours on powerful GPUs.

15. VGG19 And Resnet50 Models

16. Accuracy with pre-trained model
• We got a accuracy of 90.61% with VGG19 and 95.8% with Resnet50

17. Fine tunning Pre-trained model
• Earlier we only slightly modified the dense layer in pre-trained model
to fit out problem but doesn’t trained convolution layer. We freezed
convolution layer in keras as just tweaked dense layers.
• Now we will train some convolution layers also.

18. Accuracy with Fine tuned model
• We got a accuracy of 95.55% by tweaking last 15 convolution layers
in Resnet50 pre trained model

19. CONCLUSION
 As we learn deep neutral network helps us
to manage large amount of data with
greater accuracy.
 It is the latest algorithm used to classify
any kind of data it can be text or images.
 CNN is the advanced version of nueral
network for classifying thousands of
images having very high pixels rate and it
has been best algorithm till date.
 Nueral works on many layers as we saw
before rather than other algorithm didn’t
work or divide data into layers which
makes it more accurate and efficient.

20. Link to our kaggle kernel
Random Forest : https://www.kaggle.com/harshhj25/dogs-vs-cats
Neural Network:https://www.kaggle.com/jhashanku007/cat-vs-dog-p
rediction?scriptVersionId=4645747
CNN : https://www.kaggle.com/jhashanku007/fork-of-cat-vs-dog-prediction-
cnn?scriptVersionId=4660054
CNN with Pre tuned model: https://www.kaggle.com/jhashanku007/cat-vs-dog-cnn-with-
different-pre-trained-model/versions
CNN with tweaked layers: https://www.kaggle.com/jhashanku007/cat-vs-dog-cnn-with-
different-pre-trained-model?scriptVersionId=4686576