VGG-16 Inference with different image dimension

In this blog i will talk about how to create a classification network or fine-tune any pre-trained classification network (VGG-16) that accepts image of any dimension rather than one dimension on which it is trained. Generally a model trained for MxNx3 dimension images accept only MxNx3 input not other dimensions but here we will see how to modify network to accept different dimension input image also.

I will explain this with VGG-16 network.

(**Dimension is referred to only width and height of image/feature map not channel/depth) 

Let's talk about scenario where a single network accepting different dimensions input can be helpful.

• Case -1 You have few hundred images of smaller dimension (let's say 100x100x3) then fine-tuning a pre-trained network is one of the best option you have instead of training one from scratch.
• Case-2 You trained a classification network for a fixed input dimension (224x224x3) and at inference time you get different dimension input ( ranging from 200x200x3 to 300x300x3) and you don't want to pad or resize inference images to loose information.
• Case-3 You have multiple dataset with different input dimension then with this kind of network you can easily train a classifier without modifying input dataset.

There can be other scenario where it is helpful but let's not talk about all and move forward with solution.

So the first question is what's the problem if we pass different dimensions images to a VGG-16 network, which layer will create problem convolution or pooling or flatten or fully connect (FC) layer ??

Let's see what these layers do in brief.

• Convolution layer accept any dimension input and perform convolution with kernel values and its output dimensions depend on the padding or stride of kernel. It may or may not reduce dimension.
• Pooling layer accept any dimension input and its output dimension depend on stride of pooling operation. It will reduce dimension.
• Flatten layer accept any dimension input and its output is reshaped input in single dimension.
• Fully Connect (FC) layer accept fixed input dimension and its output dimension depend on next FC layer input dimension or output layer dimension i.e. both are fixed dimensions. It may or may not reduce dimension.

Now let's see network architecture for original VGG-16 which is trained on 224x224x3 images and same VGG-16 network when trained on 150x150x3 images.

For different dimensions of input images, after block5_pool layer feature map (feature map is nothing but the output of convolution or pooling layer of CNN ) dimensions is different because of convolution and pooling layer as they reduce feature map size by some constant factor and after that flatten layer is just flattening the feature map to one dimensional vector form.

We can see input to first FC layer is 25088 (7x7x512) when image is 224x224x3 and 8192 (4x4x512) when input is 150x150x3, so this will create a problem if you pass 150x150x3 image to a network which is trained for 224x224x3

In the above image you can see i have loaded original VGG-16 model for 1000 classes, and it gave error for 374x500x3 dimension input image, but if you uncomment resize line then it will run and give probability for 1000 classes.

So a network trained for 224x224x3 will take only 224x224x3 dimension input not 150x150x3 and vice versa. 

If we want a single network to accept both images then output dimension of flatten layer should be fixed so that FC layer should always accept the output of flatten layer. The problem will be solved if somehow we always pass fixed input dimension to FC layer And that's where Global Average Pooling Layer help us.

Conclusion till here is because FC layer accept fixed length input that's why passing different dimension image to VGG-16 network results in the error. 

Global Average Pooling Layer -

Global Average Pooling is an operation that perform average pooling of each channel of input feature map, means it's transform feature map from dimension HxWxK to 1x1xK by taking average of each channel (HxW) of feature map.

GAP Layer transforming feature map from 6x6x3 to 1x1x3 by taking average of each channel

Hence if you use GAP layer instead of flatten layer then it can handle any dimension of feature map and always produce 1x1xK dimension output where K is number of channel of input feature map which is always fixed for any CNN network. So next FC layer will always receive fixed dimension input.

As we are talking about GAP layer let's know other importance of this layer

• It is used in most of the network to handle image of different dimension 
• It is also used as a replacement of FC layer means output of GAP layer is directly fed to softmax layer
• Reduces number of trainable parameter of network and hence act as a regularizer
• Less prone to overfitting than traditional fully connected layer

Let's see VGG-16 network architecture with Global Average Pooling layer.

Here we can see in both the case output after global average pooling layer is 512 dimension vector.

Hence problem solved with GAP layer we can input smaller or even bigger image to a network if it have GAP layer.

Does this mean we can pass any dimension of image to this network ?? NO ! Why ??

You can see if a model is created for 224x224 image size then at the end before GAP layer dimension is reduced by 32 times ( 224/32 = 7 or 150/32 = 4), so our minimum dimension of input image should be greater than or equal to 32x32 image for VGG-16. 

Does this mean we can train VGG-16 having GAP layer with any image dimension (>= 32x32) ?? NO ! Why??

Now with GAP layer in VGG-16 network we can do inference with different dimension of images but not training, not directly at least, because at training time we train network in batches, batch of 32, 64 or 128 images, that means we pass multiple images to the network at the same time and if a batch contain different dimension of images then it will create a problem. Batch processing won't be able to handle different dimensions for different images. Solution for this is to create a image loader that load images of same dimensions in each batch.  

At inference time we always pass one image for inference so for inference we don't have any problem of different image dimension to network.

Notice one more thing number of parameters of network without GAP layer and with GAP layer, total parameter decreased from 138,357,544 to 37,694,248 so this proves the point that it act as a reguralizer and with GAP layer network is less prone to overfitting.

Code -

Let's see the code to create a model for inferencing different dimension input image. 

Here we are loading only convolutional block of VGG16 network not FC layer  and not passing any input dimension  for image, if you load full network with FC layer then you have to pass input dimension.

Let's say you want to train the model for 150x150x3 then at the time of data loading you have to resize the image to 150x150x3. Your model will be very accurate for this dimension but also be able to handle other dimensions images.

We can load imagenet weights, this can be helpful for fine-tuning.

We are adding Global Average Pooling Layer to network and adding two dense layer and output layer exactly same as in original VGG-16 network.

Now model is ready you can change number of classes and train on your dataset. Assuming model is trained let's see inference with this model.

Here I have passed one image without resizing and model is able to do inference on it. Original image size is 374x500x3

Let's reduce the image size.

You can see i have reduced the image dimension to 32x32x3 and still model is able to do inference.

Inference -

In my previous blog i talk about image classification and general model fine-tuning, I'm gonna use the same dataset & network and replace flatten layer to GAP layer and see how model is performing for different dimensions images.

Here are some prediction for you.
Loaded the VGG-16 network trained with GAP Layer, training images were resize to 224x224 at training time.
You can see below model is performing good for 32x32 dimension images also.

Now I passed the same image without resizing and image dimension is 499x403.

So we see here model trained for 224x224 images able to perform for different dimensions and quit accurately.

I won't say this is very great job as in real scenario it's very difficult to do correct prediction for such small images when model is trained on large dimensions images because in real scenario we get lot's of noisy data but this is quite GOOD.

Complete training and inference code is on GitHub. 

Conclusion -

• Because of flatten and fully connected layer, a CNN classification network can't process images of different dimensions.
• With global average pooling layer in any network we can do inference with different dimension images.
• Global average pooling layer reduce number of trainable parameter in network hence act as a regularizer and make network less prone to overfitting.

That's all for this blog, hope you find this blog informative.
Thanks for reading !!


Code and Model Link-

Code Link - https://github.com/111surajmaurya/VGG-16

Model File Link - https://drive.google.com/drive/folders/1H6Kgk2rIIfeZn31ecbkksRlpStqlKn00?usp=sharing

Image Classification in Keras

In this blog I will explain how to do image classification in python using Keras. Image classification is a basic problem in Deep Learning. It is a method to classify images into their respective classes.

image_1.jpg

image_2.jpg

Here is an example of Image classification, image_1.jpg should be predicted as cat and image_2.jpg should be predicted as dog by CNN (Convolutional Neural Network) model.

Keras gives us lots of CNN (VGG, ResNet, Inception etc.) models already trained on ImageNet Dataset for 1000 classes (http://www.image-net.org/), we will be using VGG-16 model and fine-tune it on dog-vs-cat dataset (https://www.kaggle.com/c/dogs-vs-cats/data) for two classes.

Data Preparation 

How to prepare Train, Validation and Test dataset ?

Train dataset is the only dataset that is used for model training (learning weights and biases) and validation dataset is used to monitor the loss and accuracy of intermediate model on unseen data. Train and validation dataset is used during training only.

After training we can choose top three model which is performing good on validation data, and test these models on the test dataset to select the best model.

We will use 80% (8000) of the total data (10000) for training purpose and 10%-10% (1000-1000) for validation and testing. It’s not a rule to use 10% of the whole dataset for testing and validation, sometimes we also use 1-2% of total images when we have millions of images.

We need three directory train, validation & test. Each directory should contain one sub-directory for each class filled with respective images.

-- train/

     -- cat/

     --dog/

--validation/

     --cat/

     --dog/

--test/

     --cat/

     --dog/

Code and Explanation

In this section i will explain code and important points regarding classification model.

First let's import all the required packages and modules.

As VGG-16 model is trained on 224x224x3 image size so we will be using same size for fine tuning however we can also fine-tune it for different size like 100x100x3 or 448x448x3, we will see how to fine-tune model for different image size in my future blogs.

As we want to modify the VGG-16 model for our own dataset we will load only convolution blocks not fully connected and output layers. This line will load the VGG-16 model with ImageNet weights without fully connected and output layers as we set “include_top” argument as false.

Original VGG-16 have two fully connected layers with 4096 neurons and output layer for 1000 classes.

As cat and dog images are not very difficult to differentiate so we will be using only 256 neurons in fully connected (FC) layer instead of 4096 neurons as in original VGG-16 network. Final prediction layer will have only one neuron for binary classification.

You can see summary of our network using model.summary() function.

Freeze Layer

Freezing any layer means we won't be modifying the weights and biases of that layer during training. This is one of the important things to do while fine tuning any already pre-trained model.

Why to freeze layers ?

As we know each layer in any network learns to detect some kind of feature like different kind of edges, corners, patches, color and template like feature from images so during fine tuning pre-trained model we can use some of the features (like edge and corner features) as it is useful for all problem statement. That's why we can freeze some layers to use already learned features.

If we freeze some layers of network that means we are modifying less layers during training process, so training and convergence process of model should be fast.

When to freeze the layers of the base model ??

One can freeze some layers during fine-tuning of pre-trained model in case of

• If you have less training images
• If new classes are similar to old classes on which model is already trained, and if new classes are totally different then one would have to train more layers of the base network like in case of fine-tuning VGG model on medical images.

Currently we are training only 30% layers of base model, If we have more images we can train more layers.

Note: Layer freezing of model should be done before model compilation.

Now let's compile the model with SGD optimizer and binary crossentropy loss. For more than two classes use categorical_crossentropy.

One can use Adam optimizer or other optimizer because some time some optimizer perform well on some data and the same optimizer with same parameters doesn’t perform well on different types of data. So it’s always better to experiment with two-three optimizers and select the one which performs well on your data.

Data Loading

Let's see how to load the data and perform augmentation using ImageDataGenerator class of Keras.

At training and validation step we want to normalize the data and at training time we also want to augment the images to increase the training dataset size. Here we are using shear, zoom and horizontal flip techniques to augment more training images.

flow_from_directory is the function that load the images from given directory, we have to pass the path of the directory containing sub-directory for each classes. Here we are using 'binary' class_mode because we have only two classes, for more than two classes use 'categorical' class_mode.

Callback

A callback is a set of functions to be applied at given stages of the training procedure. With the help of callback functions we can monitor the loss, save weights files and plot training & validation loss graphs during training.

To make things simple we will be using only ModelCheckpoint callback for now. With this callback we can save weights when their is increment in validation accuracy or decrement in validation loss controlled by monitor and mode parameter.

Enabling save_best_only parameter will save weight file if new weights are better than the previous saved weight, disabling it will save weight file of all epochs.

save_weights_only is the important parameter, if it’s true then it will save only weights of the network not the network architecture. To use this weights you have to first create the network then you can load the weights with load_weights function.

If save_weights_only is false then while saving weights it will save network architecture, training configuration and state of optimizer which allow you to resume the training where you left off. You don’t need network architecture information, you can directly load weights and network by using load_model function.

By default save_weights_only is False.

Training

Now we will use fit_generator function to start the training.

There are few terms that one should know.

Epoch - While training any deep learning model, the model is training on the whole dataset many times not just once. Epoch defined as one pass over the entire dataset. Model trained for one epoch means models have seen the whole training images once.

Batch Size - A set of N images. The samples in a batch are processed independently, in parallel. During training weights are going to be updated after each batch. Batch size can be 32, 64, 128 or 256 , depending on GPU and memory size of your system use appropriate batch size. Batch size also affects the convergence of the model I will not go in detail of this.

Steps per epoch - steps_per_epoch * batch_size = total training samples

So steps per epoch should be equal to total_training_sample divided by batch_size if you set less than the above calculated number then you will not use all training images during one epoch.

Let's train this network for 25 epoch.

Here is training summary till 8th epoch. Model achieved 98% accuracy in 6th epoch itself.

Inference
Let's see how to do inference on test images.

 Import libraries to load model and to read images.

Here we have defined the classes and loaded the model using load_model function.

In the above code we have loaded the image using PIL image processing library (I have used PIL library because Keras data loader internally uses PIL to read the images at training time), and resized the image to (224,224,3) as our model is trained for this size and then we have added one more dimension in image to make image dimension from (224,224,3) to (1,224,224,3). Model accept four dimension input, added dimension is to represent batch_size. And finally we have normalized the image by dividing it by 255.

Predict function on image for binary classification give us the probability between 0 & 1. Put a threshold on probability if less then threshold then class 0 else class 1 and here we have used 0.5 as threshold value. If model is biased toward one class then you might want to change the threshold value.

So in this blog we have seen how to fine tune VGG-16 model for new dataset & achieve good accuracy and how to use new trained model for inference on images.

That's all for this blog, hope you find this blog informative.
Thanks for reading !!

Code, used dataset  and model link are below.

Full code github link - https://github.com/111surajmaurya/VGG-16

Dataset google drive link - https://drive.google.com/drive/folders/15Et639VosTWxEmPLRqx4hdTdGvzQupbg?usp=sharing

Model link - https://drive.google.com/drive/folders/1F72YB-D4XOlIvnujE--HPj9lMUkXsjKe?usp=sharing