Designed a smaller architecture implemented from the paper Deep Residual Learning for Image Recognition and achieved 93.65% accuracy.

DigiPic-Classifier is a powerful image classification app built with Streamlit. It features two models: CIFAR-10 Object Recognition to classify objects like airplanes, cars, animals, and more, and MNIST Digit Classification for recognizing handwritten digits. With a sleek interface and real-time predictions, DigiPic-Classifier offers a seamless

Camera Calibration / Augmented Reality/ Stereo Disparity Map/ SIFT/ Training Cifar-10 Classifier Using VGG16

This project demonstrates image classification using a Convolutional Neural Network (CNN) on the CIFAR-10 dataset. The model is trained to classify images into one of 10 classes.

Tensorflow-based Object Detection on the CIFAR-10 dataset, served with FastAPI

The code does image classification using the CIFAR-10 dataset. Two models, ANN and CNN, are trained on 32x32 color images across 10 classes. Following data preprocessing, the models are constructed and trained. Their classification performance is assessed on test images, highlighting their effectiveness in identifying objects within the dataset.