Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework

Overview

This repo is the official implementation of "Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework".

@inproceedings{zhou2021instant,
  title={Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework},
  author={Zhou, Qiang and Yu, Chaohui and Wang, Zhibin and Qian, Qi and Li, Hao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4081--4090},
  year={2021}
}

The code is based on MMdetection Toolbox.

1. Install

conda create -n instant_teaching python=3.7 -y
conda activate instant_teaching
conda install pytorch=1.7.1 torchvision cudatoolkit=10.1 -c pytorch
pip install mmcv==1.2.4

git clone http://gitlab.alibaba-inc.com/jianchong.zq/InstantTeaching.git
cd InstantTeaching
pip install -v -e .

2. Prepare COCO Dataset for Semi-Supervised Learning

mkdir -p datasets/coco
ln -s [coco_2017_downloaded] datasets/coco/coco_2017

python projects/InstantTeaching/tools/split_coco.py datasets/coco/coco_2017/annotations/instances_train2017.json 10

Two files of instances_train2017_10_labeled.json and instances_train2017_90_unlabeled.json will be generated.

3. Training

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch \
    --nproc_per_node=8 --master_port=1234 tools/train.py \
    projects/InstantTeaching/configs/instant_teaching_two_stage_r50_coco_quick_10.py \
    --launcher pytorch \
    --work-dir ${output_dir} \
    --no-validate
Turning SymPy expressions into JAX functions

sympy2jax Turn SymPy expressions into parametrized, differentiable, vectorizable, JAX functions. All SymPy floats become trainable input parameters. S

Miles Cranmer 38 Dec 11, 2022
The MLOps platform for innovators 🚀

​ DS2.ai is an integrated AI operation solution that supports all stages from custom AI development to deployment. It is an AI-specialized platform service that collects data, builds a training datas

9 Jan 03, 2023
Patch Rotation: A Self-Supervised Auxiliary Task for Robustness and Accuracy of Supervised Models

Patch-Rotation(PatchRot) Patch Rotation: A Self-Supervised Auxiliary Task for Robustness and Accuracy of Supervised Models Submitted to Neurips2021 To

4 Jul 12, 2021
Torch implementation of "Enhanced Deep Residual Networks for Single Image Super-Resolution"

NTIRE2017 Super-resolution Challenge: SNU_CVLab Introduction This is our project repository for CVPR 2017 Workshop (2nd NTIRE). We, Team SNU_CVLab, (B

Bee Lim 625 Dec 30, 2022
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP

CLIP-GEN [简体中文][English] 本项目在萤火二号集群上用 PyTorch 实现了论文 《CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP》。 CLIP-GEN 是一个 Language-F

75 Dec 29, 2022
The code from the paper Character Transformations for Non-Autoregressive GEC Tagging

Character Transformations for Non-Autoregressive GEC Tagging Milan Straka, Jakub Náplava, Jana Straková Charles University Faculty of Mathematics and

ÚFAL 5 Dec 10, 2022
Prototypical Networks for Few shot Learning in PyTorch

Prototypical Networks for Few shot Learning in PyTorch Simple alternative Implementation of Prototypical Networks for Few Shot Learning (paper, code)

Orobix 835 Jan 08, 2023
Api for getting bin info and getting encrypted card details for adyen.

Bin Info And Adyen Cse Enc Python api for getting bin info and getting encrypted

Roldex Stark 8 Dec 30, 2022
Code and data for the paper "Hearing What You Cannot See"

Hearing What You Cannot See: Acoustic Vehicle Detection Around Corners Public repository of the paper "Hearing What You Cannot See: Acoustic Vehicle D

TU Delft Intelligent Vehicles 26 Jul 13, 2022
A basic duplicate image detection service using perceptual image hash functions and nearest neighbor search, implemented using faiss, fastapi, and imagehash

Duplicate Image Detection Getting Started Install dependencies pip install -r requirements.txt Run service python main.py Testing Test with pytest How

Matthew Podolak 21 Nov 11, 2022
Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR

Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR

Kai Zhang 2k Dec 31, 2022
Implementation of MeMOT - Multi-Object Tracking with Memory - in Pytorch

MeMOT - Pytorch (wip) Implementation of MeMOT - Multi-Object Tracking with Memory - in Pytorch. This paper is just one in a line of work, but importan

Phil Wang 15 May 09, 2022
Official pytorch implementation of "Scaling-up Disentanglement for Image Translation", ICCV 2021.

Official pytorch implementation of "Scaling-up Disentanglement for Image Translation", ICCV 2021.

Aviv Gabbay 41 Nov 29, 2022
Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification

Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification Suncheng Xiang Shanghai Jiao Tong University Over

SunchengXiang 68 Dec 13, 2022
使用深度学习框架提取视频硬字幕;docker容器免安装深度学习库,使用本地api接口使得界面和后端识别分离;

extract-video-subtittle 使用深度学习框架提取视频硬字幕; 本地识别无需联网; CPU识别速度可观; 容器提供API接口; 运行环境 本项目运行环境非常好搭建,我做好了docker容器免安装各种深度学习包; 提供windows界面操作; 容器为CPU版本; 视频演示 https

歌者 16 Aug 06, 2022
NEG loss implemented in pytorch

Pytorch Negative Sampling Loss Negative Sampling Loss implemented in PyTorch. Usage neg_loss = NEG_loss(num_classes, embedding_size) optimizer =

Daniil Gavrilov 123 Sep 13, 2022
SuMa++: Efficient LiDAR-based Semantic SLAM (Chen et al IROS 2019)

SuMa++: Efficient LiDAR-based Semantic SLAM This repository contains the implementation of SuMa++, which generates semantic maps only using three-dime

Photogrammetry & Robotics Bonn 701 Dec 30, 2022
5 Jan 05, 2023
Easy genetic ancestry predictions in Python

ezancestry Easily visualize your direct-to-consumer genetics next to 2500+ samples from the 1000 genomes project. Evaluate the performance of a custom

Kevin Arvai 38 Jan 02, 2023
Quickly and easily create / train a custom DeepDream model

Dream-Creator This project aims to simplify the process of creating a custom DeepDream model by using pretrained GoogleNet models and custom image dat

55 Dec 27, 2022