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Posted at - May 20, 2024

Looking to classify images using a simple dataset derived from CIFAR-10 dataset and classify the images using 1 or maybe 2 machine learning models.

You are provided with an image dataset, where there are 10 different categories of objects, each of which has 1000 images for training and 100 images for testing. Each image only contains one object. The task is to apply supervised learning algorithms to classify the testing images into 10 object categories. The code to compute image features and visualise the image is provided. You can use it to visualise the images, compute features, and transform them if necessary, e.g. using PCA and LDA. You will then perform supervised classification and report quantitative results; writing this up into a 4-page report. You don't have to use all the provided code or methods discussed in this module. You may add additional steps to the process if you wish.

You are provided with an image dataset, where there are 10 different categories of objects, each of which has 1000 images for training and 100 images for testing. Each image only contains one object. The task is to apply supervised learning algorithms to classify the testing images into 10 object categories. The code to compute image features and visualise the image is provided. You can use it to visualise the images, compute features, and transform them if necessary, e.g. using PCA and LDA. You will then perform supervised classification and report quantitative results; writing this up into a 4-page report. You don't have to use all the provided code or methods discussed in this module. You may add additional steps to the process if you wish.

**About the recuiter**Member since May 20, 2018 Tarang Patel

from Choluteca, Honduras

**Open for hiring**Apply before - Jun 19, 2024

40 hrs / week

Remote Job

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