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Deep learning model for Cats vs Dogs competition in Kaggle. Also it contains CNN's filters and feature maps visualizations.

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AkashSDas/cats-vs-dogs-the-final-showdown

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cats-vs-dogs-the-final-showdown

Dogs vs. Cats Redux: Kernels Edition is a Kaggle's competition where using the dataset you have to create classifier that can classify images as dog or cat. The evaluation metirc for this competition is cross entropy.

Table of contents

Getting started

The notebook is available on Kaggle to work in the same environment where this notebook was created i.e. use the same version packages used, etc...

Model info

Count plot for the labels

There are total 16 kernels (versions) for this project on Kaggle. Here only info for the best, latest and longest trained model info is given, if you want to look at other version then go to the main kernel and change the versions.

Model info for the best model

The best score for this competition is 3.47 (loss, kernel)

Here VGG16 model is used as the base model and the entire model is trained for 1 epoch. Below is the snapshot of the last epoch.

Model performance against testing data

Model info for the latest model

The latest score for this competition is 4.68 (loss, kernel)

Here InceptionV3 model is used as the base model and the entire model is trained for 1 epochs. Below is the snapshot of the last epoch.

Model performance against testing data

Model info for the longest trained model

The latest score for this competition is 13.72 (loss, kernel)

Here VGG16 model is used as the base model and the entire model is trained for 30 epochs. Below is the snapshot of the last epoch.

Accuracy and loss curves for this model

Model performance against testing data

Model predictions

Lastest model's predictions on testing data (part of training data)

Visualizing feature maps

If you want to see the visualizations for the filters for VGG16 and InceptionV3 then they are available in kernel_vgg16 and kernel_inceptionv3 respectively.

Feature maps for VGG16

Animal image

1st conv layer

11th conv layer

17th conv layer

Feature maps for InceptionV3

Animal image

1st conv layer

3rd conv layer

Mid conv layer

Last conv layer

Issues

Only one issue that I faced and that's during the testing(on the test data which is a part of the training data) process and use the other way to get right results.

The issue is explained in the latest kernel's Evaluation section but the summary is: while using the ImageDataGenerator for the testing data with the flow_from_dataframe method where y_col and the target_size was given, the predictions done using the ImageDataGenerator was wrong, this is because the confusion matrix for these predictions were giving reuslts (bad results) as compared to the results giving the evaluate method on tensorflow.keras.Model. So to avoid this issue I just took the images path for the testing data and use the ImageDataGenerator with the flow method on it without the labels info. After doing that the results were in sync with the results of the evaluate method on tensorflow.keras.Model.

Latest model info

Testing data evaluation results

Using the previous method

Using the new (right) method

The confusion matrix for the longest and the best models had this issue, can be viewed in their respective kernels. This issue is solved for the latest kernel.

License

APACHE LICENSE, VERSION 2.0

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Deep learning model for Cats vs Dogs competition in Kaggle. Also it contains CNN's filters and feature maps visualizations.

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