Tensorflow implementation of Image Matching with Triplet Loss on the Tiny ImageNet dataset.
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Updated
Jan 10, 2023 - Jupyter Notebook
Tensorflow implementation of Image Matching with Triplet Loss on the Tiny ImageNet dataset.
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
Demostrates a triplet loss to compute relationship between three image when one is similar to another and different from the third.
Simple pipeline of person re-identification task using metric learning via tensorflow api
Background Aware Metric Learning
Facenet--Triplet loss for image embedding using Cifar100 dataset
Use Trax Siamese deep Neural LSTM Network to predict pair of similar question (duplicates)
Qualify-As-You-Go Sensor Fusion, Process Zone Signatures and Deep Contrastive Learning for Multi-Material Composition Monitoring in LPBF Process
Pytorch Implementation of One Shot Learning for Face Recognition
End to End Face-Recognition follows the approach described in FaceNet with modifications inspired by the OpenFace project. Semi-hard triplet loss and online semi-hard triplet generator are used for further fine-tuning.
Repository for the course project done as part of CS-337 (Artificial Intelligence & Machine Learning) course at IIT Bombay in Autumn 2022.
Implementation of different Deep Learning algorithms to solve the problem of cloud classification, using images taken from the ground.
Unsupervised method for pose estimation of 2D Images using renderings of 3D models
Realtime Face Recognition using FaceNet architecture
Facial Recognition using Haar Casscade, CNN and Triplet Loss
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