Morphable Detector for Object Detection on Demand

Related tags

Deep LearningMorphable-Detector
Overview

Morphable Detector for Object Detection on Demand

(ICCV 2021) PyTorch implementation of the paper Morphable Detector for Object Detection on Demand.

teaser

If our project is helpful for your research, please consider citing:

@inproceedings{zhaomorph,
  author  = {Xiangyun Zhao, Xu Zou, Ying Wu},
  title   = {Morphable Detector for Object Detection on Demand},
  booktitle = {ICCV},
  Year  = {2021}
}

Install

First, install PyTorch and torchvision. We have tested on version of 1.8.0 with CUDA 11.0, but the other versions should also be working.

Our code is based on maskrcnn-benchmark, so you should install all dependencies.

Data Preparation

Download large scale few detection dataset here and covert the data into COCO dataset format. The file structure should look like:

  $ tree data
  dataset
  ├──fsod
      ├── annototation
      │   
      ├── images

Training (EM-like approach)

We follow FSOD Paper to pretrain the model using COCO dataset for 200,000 iterations. So, you can download the COCO pretrain model here, and use it to initilize the network.

We first initialize the prototypes using semantic vectors, then train the network run:

export NGPUS=2
RND_PORT=`shuf -i 4000-7999 -n 1`

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_port $RND_PORT --nproc_per_node=$NGPUS ./tools/train_sem_net.py \
--config-file "./configs/fsod/e2e_faster_rcnn_R_50_FPN_1x.yaml"  OUTPUT_DIR "YOUR_OUTPUT_PATH" \
MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN  2000 SOLVER.IMS_PER_BATCH 4 SOLVER.MAX_ITER 270000 \
SOLVER.STEPS "(50000,70000)" SOLVER.CHECKPOINT_PERIOD 10000 \
SOLVER.BASE_LR 0.002

Then, to update the prototypes, we first extract the features for the training samples by running:

export NGPUS=2
RND_PORT=`shuf -i 4000-7999 -n 1`

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_port $RND_PORT --nproc_per_node=$NGPUS \
./tools/train_sem_net.py --config-file "./configs/fsod/e2e_faster_rcnn_R_50_FPN_1x.yaml"  \ 
FEATURE_DIR "features" OUTPUT_DIR "WHERE_YOU_SAVE_YOUR_MODEL" \
FEATURE_SIZE 200 SEM_DIR "visual_sem.txt" GET_FEATURE True \
MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN  2000 \
SOLVER.IMS_PER_BATCH 4 SOLVER.MAX_ITER 80000 \
SOLVER.CHECKPOINT_PERIOD 10000000

To compute the mean vectors and update the prototypes, run

cd features

python mean_features.py FEATURE_FILE MEAN_FEATURE_FILE
python update_prototype.py MEAN_FEATURE_FILE

To train the network using the updated prototypes, run

export NGPUS=2
RND_PORT=`shuf -i 4000-7999 -n 1`

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_port $RND_PORT --nproc_per_node=$NGPUS \
./tools/train_sem_net.py --config-file "./configs/fsod/e2e_faster_rcnn_R_50_FPN_1x.yaml"  \
SEM_DIR "PATH_WHERE_YOU_SAVE_THE_PROTOTYPES" VISUAL True OUTPUT_DIR "WHERE_YOU_SAVE_YOUR_MODEL" \ 
MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN  2000 SOLVER.IMS_PER_BATCH 4 \
SOLVER.MAX_ITER 70000 SOLVER.STEPS "(50000,80000)" \
SOLVER.CHECKPOINT_PERIOD 10000 \
SOLVER.BASE_LR 0.002

Tests

After the model is trained, we randomly sample 5 samples for each novel category from the test data and use the mean feature vectors for the 5 samples as the prototype for that categpry. The results with different sample selection may vary a bit. To reproduce the results, we provide the features we extracted from our final model. But you can still extract your own features from your trained model.

To extract the features for test data, run

export NGPUS=2
RND_PORT=`shuf -i 4000-7999 -n 1`

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_port $RND_PORT --nproc_per_node=$NGPUS \
./tools/train_sem_net.py --config-file "./configs/fsod/e2e_faster_rcnn_R_50_FPN_1x.yaml"  \ 
FEATURE_DIR "features" OUTPUT_DIR "WHERE_YOU_SAVE_YOUR_MODEL" \
FEATURE_SIZE 200 SEM_DIR "visual_sem.txt" GET_FEATURE True \
MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN  2000 \
SOLVER.IMS_PER_BATCH 4 SOLVER.MAX_ITER 80000 \
SOLVER.CHECKPOINT_PERIOD 10000000

To compute the prototype for each class (online morphing), run

cd features

python mean_features.py FEATURE_FILE MEAN_FEATURE_FILE

Then run test,

export NGPUS=2
RND_PORT=`shuf -i 4000-7999 -n 1`

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --master_port $RND_PORT --nproc_per_node=$NGPUS ./tools/test_sem_net.py --config-file "./configs/fsod/e2e_faster_rcnn_R_50_FPN_1x.yaml" SEM_DIR WHERE_YOU_SAVE_THE_PROTOTYPES VISUAL True OUTPUT_DIR WHERE_YOU_SAVE_THE_MODEL MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN 2000 FEATURE_SIZE 200 MODEL.ROI_BOX_HEAD.NUM_CLASSES 201 TEST_SCALE 0.7

Models

Our pre-trained ResNet-50 models can be downloaded as following:

name iterations AP AP^{0.5} model Mean Features
MD 70,000 22.2 37.9 download download
name iterations AP AP^{0.5} Mean Features
MD 1-shot 70,000 19.6 33.3 download
MD 2-shot 70,000 20.9 35.7 download
MD 5-shot 70,000 22.2 37.9 download
Owner
Ph.D. student at EECS department, Northwestern University
GitHub Repository
ML course - EPFL Machine Learning Course, Fall 2021

EPFL Machine Learning Course CS-433 Machine Learning Course, Fall 2021 Repository for all lecture notes, labs and projects - resources, code templates

EPFL Machine Learning and Optimization Laboratory 1k Jan 04, 2023
Some experiments with tennis player aging curves using Hilbert space GPs in PyMC. Only experimental for now.

NOTE: This is still being developed! Setup notes This document uses Jeff Sackmann's tennis data. You can obtain it as follows: git clone https://githu

Martin Ingram 1 Jan 20, 2022
MobileNetV1-V2，MobileNeXt，GhostNet，AdderNet，ShuffleNetV1-V2，Mobile+ViT etc.

MobileNetV1-V2，MobileNeXt，GhostNet，AdderNet，ShuffleNetV1-V2，Mobile+ViT etc. ⭐⭐⭐⭐⭐

568 Jan 04, 2023
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

GCNet for Object Detection By Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, Han Hu. This repo is a official implementation of "GCNet: Non-local Networ

Jerry Jiarui XU 1.1k Dec 29, 2022
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network This repository is the official implementation of Speech Separati

Kai Li (李凯) 116 Nov 09, 2022
An implementation of based on pytorch and mmcv

FisherPruning-Pytorch An implementation of Group Fisher Pruning for Practical Network Compression based on pytorch and mmcv Main Functions Pruning f

Peng Lu 15 Dec 17, 2022
ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs

(Comet-) ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs Paper Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sa

AI2 152 Dec 27, 2022
Spectrum is an AI that uses machine learning to generate Rap song lyrics

Spectrum Spectrum is an AI that uses deep learning to generate rap song lyrics. View Demo Report Bug Request Feature Open In Colab About The Project S

39 Dec 16, 2022
NeuPy is a Tensorflow based python library for prototyping and building neural networks

NeuPy v0.8.2 NeuPy is a python library for prototyping and building neural networks. NeuPy uses Tensorflow as a computational backend for deep learnin

Yurii Shevchuk 729 Jan 03, 2023
This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes.

Polygon-Yolov5 This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes. Section I. Description The codes a

xinzelee 226 Jan 05, 2023
JstDoS - HTTP Protocol Stack Remote Code Execution Vulnerability

jstDoS If you are going to skid that, please give credits ! ^^ ¿How works? This

apolo 4 Feb 11, 2022
Motion planning environment for Sampling-based Planners

Sampling-Based Motion Planners' Testing Environment Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quick

Soraxas 23 Aug 23, 2022
Artificial intelligence technology inferring issues and logically supporting facts from raw text

개요 비정형 텍스트를 학습하여 쟁점별 사실과 논리적 근거 추론이 가능한 인공지능 원천기술 Artificial intelligence techno

6 Dec 29, 2021
Official implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis https://arxiv.org/abs/2011.13775

CIPS -- Official Pytorch Implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis Requirements pip install -r requi

Multimodal Lab @ Samsung AI Center Moscow 201 Dec 21, 2022
DSTC10 Track 2 - Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations

DSTC10 Track 2 - Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations This repository contains the data, scripts and baseline co

Alexa 51 Dec 17, 2022
The PyTorch implementation for paper "Neural Texture Extraction and Distribution for Controllable Person Image Synthesis" (CVPR2022 Oral)

ArXiv | Get Start Neural-Texture-Extraction-Distribution The PyTorch implementation for our paper "Neural Texture Extraction and Distribution for Cont

Ren Yurui 111 Dec 10, 2022
Out-of-distribution detection using the pNML regret. NeurIPS2021

OOD Detection Load conda environment conda env create -f environment.yml or install requirements: while read requirement; do conda install --yes $requ

Koby Bibas 23 Dec 02, 2022
Bi-level feature alignment for versatile image translation and manipulation (Under submission of TPAMI)

Bi-level feature alignment for versatile image translation and manipulation (Under submission of TPAMI) Preparation Clone the Synchronized-BatchNorm-P

Fangneng Zhan 12 Aug 10, 2022
This repo contains the code and data used in the paper "Wizard of Search Engine: Access to Information Through Conversations with Search Engines"

Wizard of Search Engine: Access to Information Through Conversations with Search Engines by Pengjie Ren, Zhongkun Liu, Xiaomeng Song, Hongtao Tian, Zh

19 Oct 27, 2022
[NeurIPS'21 Spotlight] PyTorch code for our paper "Aligned Structured Sparsity Learning for Efficient Image Super-Resolution"

ASSL This repository is for a new network pruning method (Aligned Structured Sparsity Learning, ASSL) for efficient single image super-resolution (SR)

Huan Wang 47 Nov 28, 2022