Dhaval Patel (DhavalkumarPatel)

DhavalkumarPatel

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Location:Toronto, Canada

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Dhaval Patel's repositories

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

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awesome-domain-adaptation

A collection of AWESOME things about domian adaptation

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CVPR2023-Papers-with-Code

CVPR 2023 论文和开源项目合集

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Deformable-DETR

Deformable DETR: Deformable Transformers for End-to-End Object Detection.

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detr

End-to-End Object Detection with Transformers

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dino

PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

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FaceRecognition-Deep-Learning-Specialization

This repository is based on a project completed as part of the Deep Learning Specialization on Coursera by DeepLearning.AI.

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ImageClassification-with-Pytorch-Ray

This MLOps repository contains python modules intended for distributed model training, tuning, and serving using PyTorch and Ray, a distributed computing framework.

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VNext

Next-generation Video instance recognition framework on top of Detectron2 which supports InstMove (CVPR 2023), SeqFormer(ECCV Oral), and IDOL(ECCV Oral))

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ImageSegmentation-Deep-Learning-Specialization

This repository is based on a project completed as part of the Deep Learning Specialization on Coursera by DeepLearning.AI.

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lightly

A python library for self-supervised learning on images.

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NeuralStyleTransfer-Deep-Learning-Specialization

This repository is based on a project completed as part of the Deep Learning Specialization on Coursera by DeepLearning.AI.

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pysindy

A package for the sparse identification of nonlinear dynamical systems from data

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sagemaker-end-to-end-workshop

Hands-on end-to-end workshop to explore Amazon SageMaker.

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SlowFast

PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

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TensorRT

NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.

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tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.

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