LabelSmoothing: address the issue of the model’s over-confidence driven by softmax.Create multiple versions of it via data augmentation and average predictions over all of them (check chapter 7 of fastbook). Test Time Augmentation (TTA): at inference time, don’t run an image through the network just once.Callbacks: access and edit properties of the model during training, to customize loss functions, plots, performance metrics and much more.Fit one cycle: smart learning rate and momentum schedule during training.Discriminative LRs: use higher rates for the final layers of the model and lower, less aggressive ones to tune the shallowest layers (e.g.Learning rate (LR) finder: stop guessing the LR for a neural network and pick the best one since the beginning.DataBlock API: package data into a model-usable format.Let’s check out the techniques/tricks/approaches I ended up experimenting with: each leaf is tagged with one category only.Īs previously mentioned, the purpose of this exercise was to explore to a maximum the breadth of possibilities offered by the new powerful API fastai offers. This is a multi-class classification problem, not a multi-label one, e.g.

Stated differently, the goal of the competition is to classify pictures of leaves into four categories: healthy OR rust OR scab OR multiple diseases. Current disease diagnosis based on human scouting is time-consuming and expensive Objectives of ‘Plant Pathology Challenge’ are to train a model using images of training dataset to Accurately classify a given image from testing dataset into different diseased category or a healthy leaf Misdiagnosis of the many diseases impacting agricultural crops can lead to misuse of chemicals leading to the emergence of resistant pathogen strains, increased input costs, and more outbreaks with significant economic loss and environmental impacts. Unsurprisingly, I turned myself towards Kaggle, and borrowed the data from the Plant Pathology competition. As always, the prerequisite for any experimenting in Machine Learning is a dataset. I am writing this post to summarize my latest efforts in exploring the Computer Vision functionality of the new fastai library.Īfter reading the first eight chapters of fastbook and attending five lectures of the 2020 course, I decided it was the right time to take a break and get my hands dirty with one of the Deep Learning applications the library offers: Computer Vision.

Fastai multilabel evaluation