This is the ROS2 package including the tools for Soft Somatosensitive Sensor (SSS).
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Updated
Feb 1, 2021 - Python
This is the ROS2 package including the tools for Soft Somatosensitive Sensor (SSS).
Leverage Metaflow, PyTorch, AWS S3, Elasticsearch, FastAPI and Docker to create a production-ready facial recognition solution. It demonstrates the practical use of deep metric learning to recognize previously unseen faces without prior training.
Create your own databse, compile tripletloss with pre-trained FaceNet model, run real-time face recognition on local host
Repository for the course project done as part of CS-337 (Artificial Intelligence & Machine Learning) course at IIT Bombay in Autumn 2022.
Background Aware Metric Learning
Qualify-As-You-Go Sensor Fusion, Process Zone Signatures and Deep Contrastive Learning for Multi-Material Composition Monitoring in LPBF Process
Facenet--Triplet loss for image embedding using Cifar100 dataset
Unsupervised method for pose estimation of 2D Images using renderings of 3D models
Facial Recognition using Haar Casscade, CNN and Triplet Loss
Pytorch Implementation of the Paper A UNIFIED VIEW OF DEEP METRIC LEARNING VIA GRADIENT ANALYSIS
This repository contains program for face recognition using transfer learning.
Learning semantic embeddings from OSM data: A Pytorch implementation of the loc2vec general method outlined in: https://sentiance.com/loc2vec-learning-location-embeddings-w-triplet-loss-networks.
Utilize Triplet Loss and mean shift based calibration for Classification - This project shows a working concept for pose classification
A Image Retrieval System
Joint Optimization of Embeddings
his repository contains an implementation for eliminating backdoor triggers embedded in images, particularly addressing poison label attacks such as Trojan, BadNets, and Blend.
SuperTriplets - Torch Supervised Metric Learning with Batch Hard Triplets Toolbox
Self-supervised learning to boost time series classification models using a triplet loss mechanism.
This project is intended to discover posssible appoaches to estimate audio similarity.
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