poppingtonic/semisupervised-rice-africa

Using semisupervised learning to classify rice diseases # Rice Disease Classification through Self-Supervised Pretraining *Brian Muhia, July 2022* In this repository, we train a self-supervised encoder on images from this Zindi challenge , and fine-tune it on the challenge task of classifying diseases in rice leaves. In the dataset, each RGB image is paired with a corresponding RGNIR (RG-Near-Infra-Red) image of the same plant. Each image has a **single** label from the set `S = ['brown', 'blast', 'healthy']`. We want to make this, and other future challenges on this type of dataset simpler by utilising **weight sharing**: representing both images of the same object with the same weights, even if they're coming from two different cameras. After that, we can use those weights for the downstream task of classifying diseases. We can achieve this through self-supervised learning. For this task, we leave the labels alone and focus on training a model of the structure **{ImageTensor , ImageTensor}**. In the standard self-supervised learning setup for computer vision, we take a single image from the training set, make a copy, suitably augment that copy, then train a neural network to **minimize the distance between copies**. When such a model is well trained to convergence, we have a **task-agnostic encoder** whose representations can be used for further downstream tasks by transfer learning. Since we have two cameras pointing at plants, we get two images per sample in the training set. We can therefore construct any self-supervised learning task by assuming the image's copy is given, and that the difference in appearance coming from the NIR channel might as well be another augmentation of the RGB image. The other augmentations from the SWAV paper/implementation are kind of a bonus but it would better to ablate them. We're also releasing one pretrained encoder from the best run, based on **XResnet-34**, which we're calling **mwalimu-128**, available here on Google Drive. Another one is in progress, based on Res2Net50 which wi …