Official PyTorch implementation of Data-free Knowledge Distillation for Object Detection, WACV 2021.

Overview

Introduction

This repository is the official PyTorch implementation of Data-free Knowledge Distillation for Object Detection, WACV 2021.

Data-free Knowledge Distillation for Object Detection
Akshay Chawla, Hongxu Yin, Pavlo Molchanov and Jose Alvarez
NVIDIA

Abstract: We present DeepInversion for Object Detection (DIODE) to enable data-free knowledge distillation for neural networks trained on the object detection task. From a data-free perspective, DIODE synthesizes images given only an off-the-shelf pre-trained detection network and without any prior domain knowledge, generator network, or pre-computed activations. DIODE relies on two key components—first, an extensive set of differentiable augmentations to improve image fidelity and distillation effectiveness. Second, a novel automated bounding box and category sampling scheme for image synthesis enabling generating a large number of images with a diverse set of spatial and category objects. The resulting images enable data-free knowledge distillation from a teacher to a student detector, initialized from scratch. In an extensive set of experiments, we demonstrate that DIODE’s ability to match the original training distribution consistently enables more effective knowledge distillation than out-of-distribution proxy datasets, which unavoidably occur in a data-free setup given the absence of the original domain knowledge.

[PDF - OpenAccess CVF]

Core idea

LICENSE

Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.

This work is made available under the Nvidia Source Code License (1-Way Commercial). To view a copy of this license, visit https://github.com/NVlabs/DIODE/blob/master/LICENSE

Setup environment

Install conda [link] python package manager then install the lpr environment and other packages as follows:

$ conda env create -f ./docker_environment/lpr_env.yml
$ conda activate lpr
$ conda install -y -c conda-forge opencv
$ conda install -y tqdm
$ git clone https://github.com/NVIDIA/apex
$ cd apex
$ pip install -v --no-cache-dir ./

Note: You may also generate a docker image based on provided Dockerfile docker_environments/Dockerfile.

How to run?

This repository allows for generating location and category conditioned images from an off-the-shelf Yolo-V3 object detection model.

  1. Download the directory DIODE_data from google cloud storage: gcs-link (234 GB)
  2. Copy pre-trained yolo-v3 checkpoint and pickle files as follows:
    $ cp /path/to/DIODE_data/pretrained/names.pkl /pathto/lpr_deep_inversion/models/yolo/
    $ cp /path/to/DIODE_data/pretrained/colors.pkl /pathto/lpr_deep_inversion/models/yolo/
    $ cp /path/to/DIODE_data/pretrained/yolov3-tiny.pt /pathto/lpr_deep_inversion/models/yolo/
    $ cp /path/to/DIODE_data/pretrained/yolov3-spp-ultralytics.pt /pathto/lpr_deep_inversion/models/yolo/
    
  3. Extract the one-box dataset (single object per image) as follows:
    $ cd /path/to/DIODE_data
    $ tar xzf onebox/onebox.tgz -C /tmp
    
  4. Confirm the folder /tmp/onebox containing the onebox dataset is present and has following directories and text file manifest.txt:
    $ cd /tmp/onebox
    $ ls
    images  labels  manifest.txt
    
  5. Generate images from yolo-v3:
    $ cd /path/to/lpr_deep_inversion
    $ chmod +x scripts/runner_yolo_multiscale.sh
    $ scripts/runner_yolo_multiscale.sh
    

Images

Notes:

  1. For ngc, use the provided bash script scripts/diode_ngc_interactivejob.sh to start an interactive ngc job with environment setup, code and data setup.
  2. To generate large dataset use bash script scripts/LINE_looped_runner_yolo.sh.
  3. Check knowledge_distillation subfolder for code for knowledge distillation using generated datasets.

Citation

@inproceedings{chawla2021diode,
	title = {Data-free Knowledge Distillation for Object Detection},
	author = {Chawla, Akshay and Yin, Hongxu and Molchanov, Pavlo and Alvarez, Jose M.},
	booktitle = {The IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
	month = January,
	year = {2021}
}
U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

Xuebin Qin 6.5k Jan 09, 2023
Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM)

Minimisation of a negative log likelihood fit to extract the lifetime of the D^0 meson (MNLL2ELDM) Introduction The average lifetime of the $D^{0}$ me

Son Gyo Jung 1 Dec 17, 2021
Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently

Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently This repository is the official implementat

VITA 4 Dec 20, 2022
Title: Heart-Failure-Classification

This Notebook is based off an open source dataset available on where I have created models to classify patients who can potentially witness heart failure on the basis of various parameters. The best

Akarsh Singh 2 Sep 13, 2022
A flexible framework of neural networks for deep learning

Chainer: A deep learning framework Website | Docs | Install Guide | Tutorials (ja) | Examples (Official, External) | Concepts | ChainerX Forum (en, ja

Chainer 5.8k Jan 06, 2023
ToFFi - Toolbox for Frequency-based Fingerprinting of Brain Signals

ToFFi Toolbox This repository contains "before peer review" version of the software related to the preprint of the publication ToFFi - Toolbox for Fre

4 Aug 31, 2022
SIEM Logstash parsing for more than hundred technologies

LogIndexer Pipeline Logstash Parsing Configurations for Elastisearch SIEM and OpenDistro for Elasticsearch SIEM Why this project exists The overhead o

146 Dec 29, 2022
Cancer metastasis detection with neural conditional random field (NCRF)

NCRF Prerequisites Data Whole slide images Annotations Patch images Model Training Testing Tissue mask Probability map Tumor localization FROC evaluat

Baidu Research 731 Jan 01, 2023
TAPEX: Table Pre-training via Learning a Neural SQL Executor

TAPEX: Table Pre-training via Learning a Neural SQL Executor The official repository which contains the code and pre-trained models for our paper TAPE

Microsoft 157 Dec 28, 2022
OntoProtein: Protein Pretraining With Ontology Embedding

OntoProtein This is the implement of the paper "OntoProtein: Protein Pretraining With Ontology Embedding". OntoProtein is an effective method that mak

ZJUNLP 80 Dec 14, 2022
A Blender python script for getting asset browser custom preview images for objects and collections.

asset_snapshot A Blender python script for getting asset browser custom preview images for objects and collections. Installation: Click the code butto

Johnny Matthews 44 Nov 29, 2022
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (CVPR 2021)

S2-BNN (Self-supervised Binary Neural Networks Using Distillation Loss) This is the official pytorch implementation of our paper: "S2-BNN: Bridging th

Zhiqiang Shen 52 Dec 24, 2022
Unsupervised Feature Ranking via Attribute Networks.

FRANe Unsupervised Feature Ranking via Attribute Networks (FRANe) converts a dataset into a network (graph) with nodes that correspond to the features

7 Sep 29, 2022
FLSim a flexible, standalone library written in PyTorch that simulates FL settings with a minimal, easy-to-use API

Federated Learning Simulator (FLSim) is a flexible, standalone core library that simulates FL settings with a minimal, easy-to-use API. FLSim is domain-agnostic and accommodates many use cases such a

Meta Research 162 Jan 02, 2023
Unsupervised clustering of high content screen samples

Microscopium Unsupervised clustering and dataset exploration for high content screens. See microscopium in action Public dataset BBBC021 from the Broa

60 Dec 05, 2022
Quasi-Dense Similarity Learning for Multiple Object Tracking, CVPR 2021 (Oral)

Quasi-Dense Tracking This is the offical implementation of paper Quasi-Dense Similarity Learning for Multiple Object Tracking. We present a trailer th

ETH VIS Research Group 327 Dec 27, 2022
Deep Hedging Demo - An Example of Using Machine Learning for Derivative Pricing.

Deep Hedging Demo Pricing Derivatives using Machine Learning 1) Jupyter version: Run ./colab/deep_hedging_colab.ipynb on Colab. 2) Gui version: Run py

Yu Man Tam 102 Jan 06, 2023
Snscrape-jsonl-urls-extractor - Extracts urls from jsonl produced by snscrape

snscrape-jsonl-urls-extractor extracts urls from jsonl produced by snscrape Usag

1 Feb 26, 2022
Neurolab is a simple and powerful Neural Network Library for Python

Neurolab Neurolab is a simple and powerful Neural Network Library for Python. Contains based neural networks, train algorithms and flexible framework

152 Dec 06, 2022
This code uses generative adversarial networks to generate diverse task allocation plans for Multi-agent teams.

Mutli-agent task allocation This code uses generative adversarial networks to generate diverse task allocation plans for Multi-agent teams. To change

Biorobotics Lab 5 Oct 12, 2022