CIFAR-10 Image Classification Model Development
Gitesh Deshmukh
CIFAR-10-Classification-Model-Neural-Network-and-Deep-Learning
Aim
To implement a particular model in order to solve the CIFAR-10 classification problem and classify every single image in terms of 1 out of 10 classes.
To build a model on training set and evaluate on tetsing set to acheive highest possible accuracy
The Model
An architecture to process images based on Convolutional Neural Networks consisting of the n Backbones (B1,...,Bn) and a Classifier.
The Backbone
The CustomModel is built with a backbone and a classifier, implementing multiple Block instances and fully connected layers in a sequential container.
The backbone is responsible for extracting features from the samples.
The Classifier
The classifier network comprises fully connected linear layers, comparing activation functions, batch normalization, and blocks. Adaptive average pooling is used to reduce the vector size and map it to a 'k' vector size.
Results
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Posted Apr 14, 2026
Developed a CNN model for CIFAR-10 image classification using deep learning techniques.
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