Over9000 optimizer

Last update: Nov 27, 2022

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Overview

Optimizers and tests

Every result is avg of 20 runs.

Dataset	LR Schedule	Imagenette size 128, 5 epoch	Imagewoof size 128, 5 epoch
Adam - baseline	OneCycle	0.8493	0.6125
RangerLars (RAdam + LARS + Lookahead)	Flat and anneal	0.8732	0.6523
Ralamb (RAdam + LARS)	Flat and anneal	0.8675	0.6367
Ranger (RAdam + Lookahead)	Flat and anneal	0.8594	0.5946
Novograd	Flat and anneal	0.8711	0.6126
Radam	Flat and anneal	0.8444	0.537
Lookahead	OneCycle	0.8578	0.6106
Lamb	OneCycle	0.8400	0.5597
DiffGrad	OneCycle	0.8527	0.5912
AdaMod	OneCycle	0.8473	0.6132

Owner

Mikhail Grankin

GitHub Repository

Code for the paper "VisualBERT: A Simple and Performant Baseline for Vision and Language"

This repository contains code for the following two papers: VisualBERT: A Simple and Performant Baseline for Vision and Language (arxiv) with a short

463 Dec 09, 2022

Easy to use Python camera interface for NVIDIA Jetson

JetCam JetCam is an easy to use Python camera interface for NVIDIA Jetson. Works with various USB and CSI cameras using Jetson's Accelerated GStreamer

358 Jan 02, 2023

A python comtrade load library accelerated by go

Comtrade-GRPC Code for python used is mainly from dparrini/python-comtrade. Just patch the code in BinaryDatReader.parse for parsing a little more eff

1 Dec 27, 2021

MEDS: Enhancing Memory Error Detection for Large-Scale Applications

MEDS: Enhancing Memory Error Detection for Large-Scale Applications Prerequisites cmake and clang Build MEDS supporting compiler $ make Build Using Do

34 Dec 14, 2022

Pytorch implementation of the paper "Topic Modeling Revisited: A Document Graph-based Neural Network Perspective"

Graph Neural Topic Model (GNTM) This is the pytorch implementation of the paper "Topic Modeling Revisited: A Document Graph-based Neural Network Persp

8 Sep 14, 2022

Local Attention - Flax module for Jax

Local Attention - Flax Autoregressive Local Attention - Flax module for Jax Install $ pip install local-attention-flax Usage from jax import random fr

16 Jun 16, 2022

This code provides a PyTorch implementation for OTTER (Optimal Transport distillation for Efficient zero-shot Recognition), as described in the paper.

Data Efficient Language-Supervised Zero-Shot Recognition with Optimal Transport Distillation This repository contains PyTorch evaluation code, trainin

45 Dec 20, 2022

Over9000 optimizer

Optimizers and tests Every result is avg of 20 runs. Dataset LR Schedule Imagenette size 128, 5 epoch Imagewoof size 128, 5 epoch Adam - baseline OneC

405 Nov 27, 2022

RM Operation can equivalently convert ResNet to VGG, which is better for pruning; and can help RepVGG perform better when the depth is large.

184 Jan 04, 2023

Face Mask Detection on Image and Video using tensorflow and keras

Face-Mask-Detection Face Mask Detection on Image and Video using tensorflow and keras Train Neural Network on face-mask dataset using tensorflow and k

12 Nov 11, 2022

[ICCV21] Self-Calibrating Neural Radiance Fields

Self-Calibrating Neural Radiance Fields, ICCV, 2021 Project Page | Paper | Video Author Information Yoonwoo Jeong [Google Scholar] Seokjun Ahn [Google

381 Dec 30, 2022

This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022).

MoEBERT This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022). Installation Create an

34 Dec 24, 2022

Tensorflow 2 implementation of the paper: Learning and Evaluating Representations for Deep One-class Classification published at ICLR 2021

Deep Representation One-class Classification (DROC). This is not an officially supported Google product. Tensorflow 2 implementation of the paper: Lea

137 Dec 23, 2022

MoveNetを用いたPythonでの姿勢推定のデモ

MoveNet-Python-Example MoveNetのPythonでの動作サンプルです。 ONNXに変換したモデルも同梱しています。変換自体を試したい方はMoveNet_tf2onnx.ipynbを使用ください。 2021/08/24時点でTensorFlow Hubで提供されている以下モデ

38 Dec 17, 2022

[ArXiv 2021] Data-Efficient Instance Generation from Instance Discrimination

InsGen - Data-Efficient Instance Generation from Instance Discrimination Data-Efficient Instance Generation from Instance Discrimination Ceyuan Yang,

93 Dec 25, 2022

Release of SPLASH: Dataset for semantic parse correction with natural language feedback in the context of text-to-SQL parsing

SPLASH: Semantic Parsing with Language Assistance from Humans SPLASH is dataset for the task of semantic parse correction with natural language feedba

35 Oct 31, 2022

Over9000 optimizer

Related tags

Overview

Optimizers and tests

Owner

Mikhail Grankin

Code for the paper "VisualBERT: A Simple and Performant Baseline for Vision and Language"

Easy to use Python camera interface for NVIDIA Jetson

A python comtrade load library accelerated by go

MEDS: Enhancing Memory Error Detection for Large-Scale Applications

Pytorch implementation of the paper "Topic Modeling Revisited: A Document Graph-based Neural Network Perspective"

Local Attention - Flax module for Jax

This code provides a PyTorch implementation for OTTER (Optimal Transport distillation for Efficient zero-shot Recognition), as described in the paper.

Over9000 optimizer

RM Operation can equivalently convert ResNet to VGG, which is better for pruning; and can help RepVGG perform better when the depth is large.

Face Mask Detection on Image and Video using tensorflow and keras

[ICCV21] Self-Calibrating Neural Radiance Fields

This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022).

Tensorflow 2 implementation of the paper: Learning and Evaluating Representations for Deep One-class Classification published at ICLR 2021

MoveNetを用いたPythonでの姿勢推定のデモ

[ArXiv 2021] Data-Efficient Instance Generation from Instance Discrimination

Release of SPLASH: Dataset for semantic parse correction with natural language feedback in the context of text-to-SQL parsing

TrackFormer: Multi-Object Tracking with Transformers

Code repository for "Free View Synthesis", ECCV 2020.

Adaptable tools to make reinforcement learning and evolutionary computation algorithms.

Learning What and Where to Draw