Xception

Xception is a short form for Extreme Inception which is a deep convolutional neural network(CNN) architecture designed to improve upon the original Inception model. It replaces standard convolution layers with depthwise separable convolutions making it more efficient and accurate for tasks like image classification. This modification reduces computational cost without compromising performance making Xception a popular choice in modern computer vision applications.

To understand Xception we first need to understand the concept of depthwise separable convolution. In traditional convolutions, each filter works with all input channels like red, green and blue in an image. After that a separate 1x1 convolution is used to combine the results from these filters. Depthwise separable convolutions break this down into two steps:

1. Depthwise Convolution: Each input channel gets its own filter and each one is processed separately.
2. Pointwise Convolution: A 1x1 convolution is applied to combine the results from all the separate filters into a single output.

Xception Architecture Overview

We divide the entire Xception architecture into three main parts: the entry flow, the middle flow and the exit flow with skip connections around the 36 layers.

1. Entry Flow

• The input image is 299×299 pixels with 3 channels (RGB).
• A 3×3 convolution layer with 32 filters is applied. This reduces the image size and captures basic features.
• ReLU activation is used to add non-linearity.
• Another 3×3 convolution with 64 filters and ReLU follows.
• The next step uses depthwise separable convolution and a 1×1 convolution layer, followed by max pooling (3×3 with stride=2) to reduce the feature map size.

2. Middle Flow

• This part is repeated 8 times.
• Each repetition involves depthwise separable convolution with 728 filters and a 3×3 kernel.
• ReLU is applied after each convolution.
• By repeating this, the model progressively extracts more complex features.

3. Exit Flow

• The final layers use separable convolutions with 728, 1024, 1536 and 2048 filters and 3×3 kernels to capture complex features.
• Global Average Pooling condenses the feature map into a single vector.
• Finally, a fully connected layer with logistic regression classifies the image.

Implementation of Xception Using Pre-Trained Xception Model in Keras

Lets see sep by step implementation of Xception:

Step 1: Importing Required Libraries

We will import tenserflow and numpy for this.

from tensorflow.keras.applications import Xception
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.xception import preprocess_input, decode_predictions
import numpy as np

Step 2: Loading the Pre-trained Xception

Here, we're loading the Xception model that has been pre-trained on the ImageNet dataset. By setting weights='imagenet', we’re using a model that already knows how to classify images based on thousands of categories like dogs, cats, cars, etc.

model = Xception(weights='imagenet')

Step 3: Loading and preprocessing the Image

• img_path: This is the path to the image we want to classify. We can replace it with the path to our own image. For the image used click here.
• image.load_img: This function loads the image and resizes it to the required size for Xception (299x299 pixels).
• image.img_to_array: Converts the image into an array (numerical format), which the model can work with.
• np.expand_dims(x, axis=0): Adds an extra dimension to the image array to match the expected input shape of the model. The model expects a batch of images, even if it’s just one image.
• preprocess_input(x): Preprocesses the image by scaling its pixel values so that they are in the correct range (similar to how the model was trained).

img_path = 'PATH_OF_IMAGE'
img = image.load_img(img_path, target_size=(299, 299))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

Input image:

Step 4: Predicting the Class of the Image

Here model predict the class of the image by processing the preprocessed image array (x). The model returns probabilities for each class, indicating the likelihood of the image belonging to each class.

predictions = model.predict(x)

Step 5: Decoding and Printing the Top Predictions

• decode_predictions(predictions, top=3): This function decodes the raw predictions into human-readable class labels. It returns the top 3 predicted classes, along with their probabilities.
• [0]: Since the model processes a batch of images (even if it's just one image), we access the first element of the result which corresponds to our image.

print('Predicted:', decode_predictions(predictions, top=3)[0])

Output:

We can see that our model is making right predictions regarding input image.

Advantages

• Efficiency: Xception uses depthwise separable convolutions, reducing computation while maintaining performance.
• Performance: It outperforms traditional models like VGG16 and ResNet on large datasets like ImageNet.
• Flexibility: Easily adapted for transfer learning making it useful even with smaller datasets.
• Scalability: Performs well on both small and large datasets, improving as the dataset grows.
• Adaptability: Xception works across multiple domains including medical image analysis and video classification.

Limitations of Xception

• Computational Resources: Training Xception from scratch needs significant computational power. Pre-trained models are recommended for limited hardware.
• Data Requirements: It performs best on large datasets. It may underperform on smaller ones unless fine-tuned with transfer learning.
• Model Size: It's deep architecture can lead to high memory usage, making it difficult to deploy on memory-constrained devices.
• Overfitting on Small Datasets: It can overfit on smaller datasets. Regularization and data augmentation help mitigate this.

Real-World Applications

• Image Classification: Xception has shown impressive results in image classification. On the ImageNet dataset Xception achieved a top-1 accuracy of 79.0% and a top-5 accuracy of 94.5%. These results are better than the earlier VGG16 and ResNet models.
• Medical Imaging: Xception's capabilities are also applied in the medical field, such as detecting Alzheimer's disease from MRI scans. One study found that the model could classify Alzheimer's at an accuracy of 99.6%, making it highly effective for medical diagnostics.
• Transfer Learning: Xception is often used as a base model for transfer learning. This means we can take a pre-trained Xception model and fine-tune it for other tasks even with smaller datasets. This approach speeds up training for new tasks and can achieve excellent results with less data.