A project involving image classification, especially using the Deep Learning Model CNN, which acquired accuracy of approx. 99%. Using this model, we did a training to classify images into 5 different classes. We also used the Machine Learning models SVM and KNN for a comparison.
A deep learning model, we aim to achieve a thorough understanding of the model's performance subtleties, as well as remarkable classification accuracy.
Support Vector Machines (SVM) are a type of supervised learning model used for classification tasks. SVM separates data points into different classes by finding the optimal hyperplane. In image classification, SVM efficiently handles high-dimensional data, effectively distinguishing between features to classify images accurately.
A machine learning model, a simple yet powerful machine learning model that operates on the principle of similarity. KNN predicts the class or value of a new data point by considering the majority label or average value of its nearest neighbors from the training dataset.
For this image classification, we used a rice types images dataset from Kaggle, which is the following : https://www.kaggle.com/datasets/muratkokludataset/rice-image-dataset
It contains 75 000 color images (RGB color space -Red, Green and Blue-) with 5 classes (rice types), each class contains 15 000 images. To explore this project, please verify you are working on a GPU device for afaster training and better performance. The evaluation part can be done on CPU.
Insert unseen images, in this case a rice image, and get a prediction of it's label. The project is submitted as a Jupyter notebook which includes Data preprocessing, building a model, model training and evaluation, as well as some visualization. Based on the accuracy of each model, we deduced that the best model is CNN model, which is a Deep Learning model.
for each model, we worked with different Framework. for CNN model, we used the Pytorch Framework. Since it is the case, the preprocessing part is different, where it needs to follow each Framework as well as the type of model used.
If you need support, you can follow the contributors on GitHub, using their username: Djihene Beladjine : DjiheneBA ; Yilun Du : Yilun-D ; Christy Lazar : lazarchris ;
Thank you.