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
Curating a dataset for bioimage transfer learning

CytoImageNet A large-scale pretraining dataset for bioimage transfer learning. Motivation In past few decades, the increase in speed of data collectio

Stanley Z. Hua 9 Jun 20, 2022
Final Project for the CS238: Decision Making Under Uncertainty course at Stanford University in Autumn '21.

Final Project for the CS238: Decision Making Under Uncertainty course at Stanford University in Autumn '21. We optimized wind turbine placement in a wind farm, subject to wake effects, using Q-learni

Manasi Sharma 2 Sep 27, 2022
Assessing syntactic abilities of BERT

BERT-Syntax Assesing the syntactic abilities of BERT. What Evaluate Google's BERT-Base and BERT-Large models on the syntactic agreement datasets from

Yoav Goldberg 147 Aug 02, 2022
FrankMocap: A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator

FrankMocap pursues an easy-to-use single view 3D motion capture system developed by Facebook AI Research (FAIR). FrankMocap provides state-of-the-art 3D pose estimation outputs for body, hand, and bo

Facebook Research 1.9k Jan 07, 2023
Official PyTorch implementation of "Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning" (ICCV2021 Oral)

MeTAL - Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning (ICCV2021 Oral) Sungyong Baik, Janghoon Choi, Heewon Kim, Dohee Cho, Jaes

Sungyong Baik 44 Dec 29, 2022
PyTorch implementation of popular datasets and models in remote sensing

PyTorch Remote Sensing (torchrs) (WIP) PyTorch implementation of popular datasets and models in remote sensing tasks (Change Detection, Image Super Re

isaac 222 Dec 28, 2022
This project is a re-implementation of MASTER: Multi-Aspect Non-local Network for Scene Text Recognition by MMOCR

This project is a re-implementation of MASTER: Multi-Aspect Non-local Network for Scene Text Recognition by MMOCR,which is an open-source toolbox based on PyTorch. The overall architecture will be sh

Jianquan Ye 82 Nov 17, 2022
Motion and Shape Capture from Sparse Markers

MoSh++ This repository contains the official chumpy implementation of mocap body solver used for AMASS: AMASS: Archive of Motion Capture as Surface Sh

Nima Ghorbani 135 Dec 23, 2022
Open source Python implementation of the HDR+ photography pipeline

hdrplus-python Open source Python implementation of the HDR+ photography pipeline, originally developped by Google and presented in a 2016 article. Th

77 Jan 05, 2023
《Single Image Reflection Removal Beyond Linearity》(CVPR 2019)

Single-Image-Reflection-Removal-Beyond-Linearity Paper Single Image Reflection Removal Beyond Linearity. Qiang Wen, Yinjie Tan, Jing Qin, Wenxi Liu, G

Qiang Wen 51 Jun 24, 2022
Using this codebase as a tool for my own research. Making some modifications to the original repo for my own purposes.

For SwapNet Create a list.txt file containing all the images to process. This can be done with the GNU find command: find path/to/input/folder -name '

Andrew Jong 2 Nov 10, 2021
code for the ICLR'22 paper: On Robust Prefix-Tuning for Text Classification

On Robust Prefix-Tuning for Text Classification Prefix-tuning has drawed much attention as it is a parameter-efficient and modular alternative to adap

Zonghan Yang 12 Nov 30, 2022
PyTorch implementation of Value Iteration Networks (VIN): Clean, Simple and Modular. Visualization in Visdom.

VIN: Value Iteration Networks This is an implementation of Value Iteration Networks (VIN) in PyTorch to reproduce the results.(TensorFlow version) Key

Xingdong Zuo 215 Dec 07, 2022
Focal Loss for Dense Rotation Object Detection

Convert ResNets weights from GluonCV to Tensorflow Abstract GluonCV released some new resnet pre-training weights and designed some new resnets (such

17 Nov 24, 2021
An official implementation of "Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentation" (ICCV 2021) in PyTorch.

Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentation This is an official implementation of the paper "Exploiting a Joint

CV Lab @ Yonsei University 35 Oct 26, 2022
Python implementation of 3D facial mesh exaggeration using the techniques described in the paper: Computational Caricaturization of Surfaces.

Python implementation of 3D facial mesh exaggeration using the techniques described in the paper: Computational Caricaturization of Surfaces.

Wonjong Jang 8 Nov 01, 2022
City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces

City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces Paper Temporary GitHub page for City Surfaces paper. More soon! While designing s

14 Nov 10, 2022
Improving XGBoost survival analysis with embeddings and debiased estimators

xgbse: XGBoost Survival Embeddings "There are two cultures in the use of statistical modeling to reach conclusions from data

Loft 242 Dec 30, 2022
Docker containers of baseline agents for the Crafter environment

Crafter Baselines This repository contains Docker containers for running various baselines on the Crafter environment. Reward Agents DreamerV2 based o

Danijar Hafner 17 Sep 25, 2022
Deep Reinforcement Learning based Trading Agent for Bitcoin

Deep Trading Agent Deep Reinforcement Learning based Trading Agent for Bitcoin using DeepSense Network for Q function approximation. For complete deta

Kartikay Garg 669 Dec 29, 2022