mmdetection version of TinyBenchmark.

Overview

introduction

This project is an mmdetection version of TinyBenchmark.

TODO list:

  • add TinyPerson dataset and evaluation
  • add crop and merge for image during inference
  • implement RetinaNet and Faster-FPN baseline on TinyPerson
  • add SM/MSM experiment support
  • add visDronePerson dataset support and baseline performance
  • add point localization task for TinyPerson
  • add point localization task for visDronePerson
  • add point localization task for COCO

install and setup

download project

git clone https://github.com/ucas-vg/TOV_mmdetection --recursive

install mmdetection

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab
conda install -c pytorch pytorch=1.5.0 cudatoolkit=10.2 torchvision -y  # (recommand)
# install latest pytorch prebuilt with the default prebuilt CUDA version (usually the latest)
# conda install -c pytorch pytorch torchvision -y

# install the latest mmcv
pip install mmcv-full --user
# install mmdetection
cd TOV_mmdetection
pip uninstall pycocotools
pip install -r requirements/build.txt
pip install -v -e . --user  # or "python setup.py develop"

For more detail, please refer mmdetection install to install mmdetecion.

Quickly Start

to train baseline of TinyPerson, download the mini_annotation of all annotation is enough, which can be downloaded as tiny_set/mini_annotations.tar.gz in Baidu Yun(password:pmcq) / Google Driver.

mkdir data
ln -s $Path of TinyPerson$ data/tiny_set
tar -zxvf data/tiny_set/mini_annotations.tar.gz && mv mini_annotations data/tiny_set/

# run experiment, for other config run, see exp/Baseline_TinyPerson.sh
export GPU=4 && LR=02 && CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=10000 tools/dist_train.sh configs2/TinyPerson/base/faster_rcnn_r50_fpn_1x_TinyPerson640.py $GPU \
  --work-dir ../TOV_mmdetection_cache/work_dir/TinyPerson/Base/faster_rcnn_r50_fpn_1x_TinyPerson640/old640x512_lr0${LR}_1x_${GPU}g/ \
  --cfg-options optimizer.lr=0.${LR}

performance

All train and test on 2080Ti,

  • CUDA10.1/10.2
  • python3.7, cudatookit=10.2, pytorch=1.5, torchvision=0.6

for Faster-FPN, we think the gain compare to TinyBenchmark may come from the cut and merge during inference running time and multi-gpu training.

performance 43.80(2) where 2 means the performance is mean result of running such setting for 2 time.

detector num_gpu $AP_{50}^{tiny}$ script
Faster-FPN 4 48.63(1) exp/Baseline_TinyPerson.sh:exp1.1
Adap RetainaNet 1 43.80(2) exp/Baseline_TinyPerson.sh:exp2.1
Adap RetainaNet 4 44.94(1) exp/Baseline_TinyPerson.sh:exp2.2(clip grad)
Deep Learning Slide Captcha

滑动验证码深度学习识别 本项目使用深度学习 YOLOV3 模型来识别滑动验证码缺口,基于 https://github.com/eriklindernoren/PyTorch-YOLOv3 修改。 只需要几百张缺口标注图片即可训练出精度高的识别模型,识别效果样例: 克隆项目 运行命令: git cl

Python3WebSpider 55 Jan 02, 2023
Official Pytorch implementation for AAAI2021 paper (RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning)

RSPNet Official Pytorch implementation for AAAI2021 paper "RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning" [Suppleme

35 Jun 24, 2022
Source code of our BMVC 2021 paper: AniFormer: Data-driven 3D Animation with Transformer

AniFormer This is the PyTorch implementation of our BMVC 2021 paper AniFormer: Data-driven 3D Animation with Transformer. Haoyu Chen, Hao Tang, Nicu S

24 Nov 02, 2022
PyTorch implementation of "Image-to-Image Translation Using Conditional Adversarial Networks".

pix2pix-pytorch PyTorch implementation of Image-to-Image Translation Using Conditional Adversarial Networks. Based on pix2pix by Phillip Isola et al.

mrzhu 383 Dec 17, 2022
Explaining neural decisions contrastively to alternative decisions.

Contrastive Explanations for Model Interpretability This is the repository for the paper "Contrastive Explanations for Model Interpretability", about

AI2 16 Oct 16, 2022
ppo_pytorch_cpp - an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch

PPO Pytorch C++ This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment t

Martin Huber 59 Dec 09, 2022
code for Fast Point Cloud Registration with Optimal Transport

robot This is the repository for the paper "Accurate Point Cloud Registration with Robust Optimal Transport". We are in the process of refactoring the

28 Jan 04, 2023
Extracts essential Mediapipe face landmarks and arranges them in a sequenced order.

simplified_mediapipe_face_landmarks Extracts essential Mediapipe face landmarks and arranges them in a sequenced order. The default 478 Mediapipe face

Irfan 13 Oct 04, 2022
Open source implementation of "A Self-Supervised Descriptor for Image Copy Detection" (SSCD).

A Self-Supervised Descriptor for Image Copy Detection (SSCD) This is the open-source codebase for "A Self-Supervised Descriptor for Image Copy Detecti

Meta Research 68 Jan 04, 2023
Points2Surf: Learning Implicit Surfaces from Point Clouds (ECCV 2020 Spotlight)

Points2Surf: Learning Implicit Surfaces from Point Clouds (ECCV 2020 Spotlight)

Philipp Erler 329 Jan 06, 2023
Pip-package for trajectory benchmarking from "Be your own Benchmark: No-Reference Trajectory Metric on Registered Point Clouds", ECMR'21

Map Metrics for Trajectory Quality Map metrics toolkit provides a set of metrics to quantitatively evaluate trajectory quality via estimating consiste

Mobile Robotics Lab. at Skoltech 31 Oct 28, 2022
Progressive Growing of GANs for Improved Quality, Stability, and Variation

Progressive Growing of GANs for Improved Quality, Stability, and Variation — Official TensorFlow implementation of the ICLR 2018 paper Tero Karras (NV

Tero Karras 5.9k Jan 05, 2023
Official Implementation of "Learning Disentangled Behavior Embeddings"

DBE: Disentangled-Behavior-Embedding Official implementation of Learning Disentangled Behavior Embeddings (NeurIPS 2021). Environment requirement The

Mishne Lab 12 Sep 28, 2022
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing

FairEdit Relevent Publication FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing

5 Feb 04, 2022
SEC'21: Sparse Bitmap Compression for Memory-Efficient Training onthe Edge

Training Deep Learning Models on The Edge Training on the Edge enables continuous learning from new data for deployed neural networks on memory-constr

Brown University Scale Lab 4 Nov 18, 2022
Code for the paper One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation, CVPR 2021.

One Thing One Click One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation (CVPR2021) Code for the paper One Thi

44 Dec 12, 2022
A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm

Multi-Agent-Deep-Deterministic-Policy-Gradients A Pytorch implementation of the multi agent deep deterministic policy gradients(MADDPG) algorithm This

Phil Tabor 159 Dec 28, 2022
PyTorch trainer and model for Sequence Classification

PyTorch-trainer-and-model-for-Sequence-Classification After cloning the repository, modify your training data so that the training data is a .csv file

NhanTieu 2 Dec 09, 2022
UFT - Universal File Transfer With Python

UFT 2.0.0 UFT (Universal File Transfer) is a CLI tool , which can be used to upl

Merwin 1 Feb 18, 2022
Towards Fine-Grained Reasoning for Fake News Detection

FinerFact This is the PyTorch implementation for the FinerFact model in the AAAI 2022 paper Towards Fine-Grained Reasoning for Fake News Detection (Ar

Ahren_Jin 15 Dec 15, 2022