The MNIST Dataset

In this example, we used the MNIST dataset (handwritten digits) to train a convolutional neural network (CNN) for image classification, and reached a validation accuracy of ~0.98 on our trained model.

In this example, we used the MNIST dataset (handwritten digits) to train a convolutional neural network (CNN) for image classification, and reached a validation accuracy of ~0.98 on our trained model.

LibriSpeech Dataset With Wav2vec

stt-wav2vec2-librispeech

A speech recognition task for predicting the text from an audio file. We utilize a wav2vec model trained on the librispeech-asr benchmark, implemented using Keras and Tensorflow.

Sentiment Analysis

The IMDB dataset is one of the most popular sentiment analysis datasets. It contains multiple movie reviews, each annotated with either a positive or negative label. In this example, a classifier model was built to predict positive and negative reviews.

Detecting Pneumonia – Chest X-Ray Images

The project uses transfer learning from a pre-trained DensNet201 network to classify chest X-ray images into one of three classes- normal, bacteria, or virus. In a DenseNet, we use dense connections between layers, using Dense Blocks, where each layer (with identical feature maps) directly connects with each other through dense connections. On the left panel of the chest X-ray […]

Cifar10 – Resnet

This project implements the resnet algorithm using CIFAR-10 dataset for image classification tasks.

CityScapes – Object Detection

This project utilizes the YOLOv7 model to perform object detection task on Cityscapes dataset. The cityscapes dataset is a large-scale dataset that stands as one of the standard advanced driver-assistance system (ADAS) benchmarks for multiple vision-related tasks.

Optical Flow – Raft

This project utilizes the RAFT (Recurrent All-pairs Field Transforms) model that estimates Optical Flow using the KITTI dataset.