A full-fledged version of Pix2Seq

Overview

Stable-Pix2Seq

A full-fledged version of Pix2Seq

What it is. This is a full-fledged version of Pix2Seq. Compared with unofficial-pix2seq, stable-pix2seq contain most of the tricks mentioned in Pix2Seq like Sequence Augmentation, Batch Repretation, Warmup, Linear decay leanring rate and beam search(to be add later).

Difference between Pix2Seq. In sequence augmentation, we only augment random bounding box while original paper will mix with virual box from ground truth plus noise. Pix2seq also use input sequence dropout to regularize the training process.

Usage - Object detection

There are no extra compiled components in Stable-Pix2Seq and package dependencies are minimal, so the code is very simple to use. We provide instructions how to install dependencies via conda. First, clone the repository locally:

git clone https://github.com/gaopengcuhk/Stable-Pix2Seq.git

Then, install PyTorch 1.5+ and torchvision 0.6+:

conda install -c pytorch pytorch torchvision

Install pycocotools (for evaluation on COCO) and scipy (for training):

conda install cython scipy
pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

That's it, should be good to train and evaluate detection models.

Data preparation

Download and extract COCO 2017 train and val images with annotations from http://cocodataset.org. We expect the directory structure to be the following:

path/to/coco/
  annotations/  # annotation json files
  train2017/    # train images
  val2017/      # val images

Training

To train baseline Stable-Pix2Seq on a single node with 8 gpus for 300 epochs run:

python -m torch.distributed.launch --master_port=3141 --nproc_per_node 8 --use_env main.py --coco_path ./coco/ --batch_size 4 --lr 0.0005

A single epoch takes 50 minutes on 8 V100, so 300 epoch training takes around 10 days on a single machine with 8 V100 cards.

Why slower than DETR and Unofficial-Pix2Seq?. Stable-Pix2Seq use batch repeat which double the training time. Besides, stable-pix2seq use 1333 image resolution will the time report in unofficial-pix2seq is trained on low resolution 512.

We train DETR with AdamW setting learning rate using a linear warmup and decay schedule. Due to batch repeat, the real barch size is 64. Horizontal flips, scales and crops are used for augmentation. Images are rescaled to have min size 800 and max size 1333. The transformer is trained with dropout of 0.1, and the whole model is trained with grad clip of 0.1.

Please use the learning rate 0.0005 with causion. It is tested on batch 198.

Evaluation

To evaluate Stable-Pix2Seq R50 on COCO val5k with multiple GPU run:

python -m torch.distributed.launch --master_port=3142 --nproc_per_node 8 --use_env main.py --coco_path ./coco/ --batch_size 4 --eval --resume checkpoint.pth

Acknowledgement

DETR

Owner
peng gao
Young Scientist at Shanghai AI Lab
peng gao
Lightwood is Legos for Machine Learning.

Lightwood is like Legos for Machine Learning. A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glu

MindsDB Inc 312 Jan 08, 2023
2D Human Pose estimation using transformers. Implementation in Pytorch

PE-former: Pose Estimation Transformer Vision transformer architectures perform very well for image classification tasks. Efforts to solve more challe

Panteleris Paschalis 23 Oct 17, 2022
Code for the paper 'A High Performance CRF Model for Clothes Parsing'.

Clothes Parsing Overview This code provides an implementation of the research paper: A High Performance CRF Model for Clothes Parsing Edgar Simo-S

Edgar Simo-Serra 119 Nov 21, 2022
The backbone CSPDarkNet of YOLOX.

YOLOX-Backbone The backbone CSPDarkNet of YOLOX. In this project, you can enjoy: CSPDarkNet-S CSPDarkNet-M CSPDarkNet-L CSPDarkNet-X CSPDarkNet-Tiny C

Jianhua Yang 9 Aug 22, 2022
PyTorch implementation of EfficientNetV2

[NEW!] Check out our latest work involution accepted to CVPR'21 that introduces a new neural operator, other than convolution and self-attention. PyTo

Duo Li 375 Jan 03, 2023
PURE: End-to-End Relation Extraction

PURE: End-to-End Relation Extraction This repository contains (PyTorch) code and pre-trained models for PURE (the Princeton University Relation Extrac

Princeton Natural Language Processing 657 Jan 09, 2023
CVPR 2021 Official Pytorch Code for UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training

UC2 UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training Mingyang Zhou, Luowei Zhou, Shuohang Wang, Yu Cheng, Linjie Li, Zhou Yu,

Mingyang Zhou 28 Dec 30, 2022
SLAMP: Stochastic Latent Appearance and Motion Prediction

SLAMP: Stochastic Latent Appearance and Motion Prediction Official implementation of the paper SLAMP: Stochastic Latent Appearance and Motion Predicti

Kaan Akan 34 Dec 08, 2022
PyTorch implementation of paper: HPNet: Deep Primitive Segmentation Using Hybrid Representations.

HPNet This repository contains the PyTorch implementation of paper: HPNet: Deep Primitive Segmentation Using Hybrid Representations. Installation The

Siming Yan 42 Dec 07, 2022
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers

Official TensorFlow implementation of the unsupervised reconstruction model using zero-Shot Learned Adversarial TransformERs (SLATER). (https://arxiv.

ICON Lab 22 Dec 22, 2022
[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation [Paper] Prerequisites To install requirements: pip install -r requirements.txt

Guangrui Li 84 Dec 26, 2022
Official repo for SemanticGAN https://nv-tlabs.github.io/semanticGAN/

SemanticGAN This is the official code for: Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizat

151 Dec 28, 2022
Tutorial: Introduction to Graph Machine Learning, with Jupyter notebooks

GraphMLTutorialNLDL22 Tutorial NLDL22: Introduction to Graph Machine Learning, with Jupyter notebooks This tutorial takes place during the conference

UiT Machine Learning Group 3 Jan 10, 2022
Phonetic PosteriorGram (PPG)-Based Voice Conversion (VC)

ppg-vc Phonetic PosteriorGram (PPG)-Based Voice Conversion (VC) This repo implements different kinds of PPG-based VC models. Pretrained models. More m

Liu Songxiang 227 Dec 28, 2022
A fast and easy to use, moddable, Python based Minecraft server!

PyMine PyMine - The fastest, easiest to use, Python-based Minecraft Server! Features Note: This list is not always up to date, and doesn't contain all

PyMine 144 Dec 30, 2022
A deep learning object detector framework written in Python for supporting Land Search and Rescue Missions.

AIR: Aerial Inspection RetinaNet for supporting Land Search and Rescue Missions AIR is a deep learning based object detection solution to automate the

Accenture 13 Dec 22, 2022
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

Alpha Zero General (any game, any framework!) A simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play

Surag Nair 3.1k Jan 05, 2023
Some methods for comparing network representations in deep learning and neuroscience.

Generalized Shape Metrics on Neural Representations In neuroscience and in deep learning, quantifying the (dis)similarity of neural representations ac

Alex Williams 45 Dec 27, 2022
Progressive Domain Adaptation for Object Detection

Progressive Domain Adaptation for Object Detection Implementation of our paper Progressive Domain Adaptation for Object Detection, based on pytorch-fa

96 Nov 25, 2022
The fastai book, published as Jupyter Notebooks

English / Spanish / Korean / Chinese / Bengali / Indonesian The fastai book These notebooks cover an introduction to deep learning, fastai, and PyTorc

fast.ai 17k Jan 07, 2023