Anomaly Detection with Computer Vision || Deep Learning Pytorch Project || PART 2

In the previous part of this series, we discussed the basics of anomaly detection using computer vision and deep learning with PyTorch. In this part, we will delve deeper into the implementation of the project and the different techniques used.

Preparing the Dataset

First, we need to prepare a dataset that contains normal images as well as anomalous images. We will use a combination of images from the MNIST dataset and artificially created anomalous images for our training and testing sets.

Building the Model

We will use a convolutional neural network (CNN) as our model for anomaly detection. The CNN will be trained on normal images and will learn to recognize patterns and features in these images. During testing, if the model encounters an anomalous image, it will be unable to recognize the pattern and flag it as an anomaly.

Training the Model

Once the dataset and model are prepared, we can start training the model. We will use the PyTorch framework to define our model, loss function, and optimizer. We will also split our dataset into training and testing sets to evaluate the performance of the model.

Evaluating the Model

After training the model, we will evaluate its performance on the testing set. We will calculate metrics such as accuracy, precision, recall, and F1 score to determine how well the model can detect anomalies in the images.

Conclusion

In this part of the series, we have discussed the implementation of the anomaly detection project using computer vision and deep learning with PyTorch. By building and training a CNN model on normal and anomalous images, we can effectively detect anomalies in the images. In the next part, we will explore ways to improve the model’s performance and optimize its accuracy.