Computer Vision

PyTorch Basic Tutorial

PyTorch libraries torchvision: for computer vision torchtext: for NLP torchaudio: for speech

Computer Vision

Color and color spaces in Computer Vision

A picture is worth a millions words.

Deep Learning

Introduction to Panoptic Segmentation: A Tutorial

In semantic segmentation, the goal is to classify each pixel into the given classes. In instance segmentation, we care about segmentation of the instances of objects separately. The...

Deep Learning

Evaluation metrics for object detection and segmentation: mAP

Read about semantic segmentation, and instance segmentation.

Deep Learning

Quick intro to Instance segmentation: Mask R-CNN

This is the third post in the Quick intro series: object detection (I), semantic segmentation (II).

Deep Learning

Quick intro to semantic segmentation: FCN, U-Net and DeepLab

Suppose you’ve an image, consisting of cats. You want to classify every pixel of the image as cat or background. This process is called semantic segmentation.

Deep Learning

Converting FC layers to CONV layers

It is worth noting that the only difference between FC and CONV layers is that the neurons in the CONV layer are connected only to a local region...

Deep Learning

Data augmentation

Deep Learning

Generative Adversarial Networks variants: DCGAN, Pix2pix, CycleGAN

First, make sure you read the first part of this post, Generative models and Generative Adversarial Networks. This post is its continuation.

Deep Learning

Quick intro to Object detection: R-CNN, YOLO, and SSD

Object detection deals with the detection of object instances in an image. There are a number of methods to accomplish it. The following post summarizes few important object...

Deep Learning

Attention

Attention is the technique through which the model focuses itself on a certain region of the image or on certain words in a sentence just like the same...

Deep Learning

Image captioning using encoder-decoder

Note: This post discusses the project I’m doing currently. Check it on kHarshit/image-captioner. The working demo is available on https://imagecaptioner.herokuapp.com.

Deep Learning

Why Batch Normalization?

As mentioned in Scaling Vs Normalization, applying normalization to the input features increases the convergence rate of our algorithm i.e. it speeds up the learning process.

Deep Learning

Filters in Convolutional Neural Networks

Note: This post is inspired by the answer I gave on stackoverflow.

Deep Learning

Generative models and Generative Adversarial Networks

In supervised learning, we have data x and response (label) y and the goal is to learn a function to map x to y e.g. regression, classification, object...

Deep Learning

Skip connections and Residual blocks

Deep neural networks are difficult to train. They also have vanishing or exploding gradient problems. Batch normalization helps, but with the increase in depth, the network has trouble...

Deep Learning

Transfer learning: How to build accurate models

A good Covolutional Neural Network model requires a large dataset and good amount of training, which is often not possible in practice. Transfer learning provides a turn around...

Deep Learning

The magic behind ConvNets

Ordinary neural networks consists of neurons that have learnable weights and biases. The input is a single vector (of features) that is transfomed through a number of hidden...