Difference The terms “online prediction” and “batch prediction” can be confusing. Both can make predictions for multiple samples or one sample at a time and can make predictions asynchronously(when online prediction leverages a real-time transport to send prediction requests to…
Components When people say “Machine Learning”, they usually think of “Machine Learning Algorithms”, but actually, algorithms are only a small part of the entire system. Here is an overview of what is necessary to compose a Machine Learning System. Reference…
Difference Transfer learning and Fine-tuning both replace the final layer to make predictions for a different task. However, while transfer learning fixes all the layer previous to the final output layer, fine-tuning initializes the weights with the pre-trained weights but…
Preparing Your Dataset For Training When training a multi-class segmentation model, loss functions such as the CrossEntropyLoss() in Pytorch require the mask to be categorically labeled. As in the image above, the RGB mask (middle) is properly segmented but all…
When applying data augmentation for computer vision models, it is important to apply the same augmentation in the same order for both the input and target image. Here is one way to implement using Albumentation and Pytorch. Implementation Today I’ll…
Tradeoffs When training a model, there is always a tradeoff between the model having bias and variance. When a model is suffering from high bias, it means that it is UNDER-fitting to the data and is not able to make…
Training Your Model When training a model, the dataset is often divided into a Training set, a Validation Set, and a Test Set. The ratio to split the data into these 3 sets depends on how large your dataset is,…
Clustering Depending on your data and objective, you may not even have to train a deep-learning model for image segmentation. Here is one way to apply segmentation using Kmeans clustering. Implementation from sklearn.cluster import KMeans from matplotlib.image import imread import…
Implementation Saving checkpoints for model weights during training can be helpful in the case of the following examples. Want to resume training later Avoid losing weight data when the process stops during training due to some kind of error. Restore…
Anomaly Detection Anomaly detection in videos refers to the identification of events that do not align with the expected behavior. This paper is a pioneering work that leverages the difference between a predicted future frame and its ground truth to…