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adityassrana.github.io

bits-f312-neural-networks-and-fuzzy-logic

Archived Notebooks from Neural Networks course at BITS Pilani. Course website at https://bitsnnfl.github.io/

blog

https://adityassrana.github.io/blog

content-weighted-image-compression

PyTorch implementation of Learning Convolutional Networks for Content-Weighted Image Compression

fastai_playground

Experiments with things I learned while doing the fast.ai course.

mcv-m1-ihcv

Content Based Image Retrieval. Implementations of color histograms, spatial pyramids, HOG, DCT, LBP, top hat and low hat filters, background and text removal, image denoising, and keypoints descriptors SIFT, SURF, ORB.

mcv-m2-oicv

Optimization in Computer Vision. Implementations of Image Inpainting, Poisson Editing, Chan-Vese Segmentation and Markov Random Fields for Image Segmentation.

mcv-m3-mlcv

Image classification - Bag of Visual Words, Keypoints and Descriptors, Spatial Pyramids, K-Means and GMM clustering, PCA, LDA, SVM, Fisher Vectors, Fine Tuning CNNs, Training CNNs from scratch.

mcv-m4-3dv

Implemented image warping, affine and metric rectification, DLT, RANSAC, panorama stitching Zhang’s calibration method, view morphing, stereo matching, depth-map computation, bundle adjustment and resectioning for Structure from Motion

mcv-m5-vr

Detectron2 implementations of object detection and instance segmentation on KITTI, KITTI-MOTS and MOTS Challenge datasets. Extensively experimented with different Faster-RCNN, RetinaNet and Mask-RCNN architectures available in the Model Zoo.

mcv-m6-va

Weekly progress made on the task of Multi-Target Multi-Camera Tracking by implementing background modelling, object detection, IoU and Kalman tracking, block matching optical flow, video stabilization, metric learning using Siamese networks and tracklet matching algorithms.

mobile-compression

Efficient architectures for image compression

multimodal-learning

RGBD-Scene Classification. Inroductory problem to get started with multimodal deep learning

object-detection-mnist

Toy problem to get started with object detection and bounding box regression using MNIST

old_blog_template

pytorch-image-classification

A script to quickly evaluate image classification performance on different architectures, optimizers and datasetss