This repository contains the DendroMap implementation for scalable and interactive exploration of image datasets in machine learning.

Overview

DendroMap

DendroMap is an interactive tool to explore large-scale image datasets used for machine learning.

A deep understanding of your data can be vital to train or debug your model effectively. However, due to the lack of structure and little-to-no metadata, it can be difficult to gain any insight into large-scale image datasets.

DendroMap adds structure to the data by hierarchically clustering together similar images. Then, the clusters are displayed in a modified treemap visualization that supports zooming.

Check out the live demo of DendroMap and explore for yourself on a few different datasets. If you're interested in

  • the DendroMap motivations
  • how we created the DendroMap visualization
  • DendroMap's effectiveness: user study on DendroMap compared to t-SNE grid for exploration

be sure to also check out our research paper:

Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps.
Donald Bertucci, Md Montaser Hamid, Yashwanthi Anand, Anita Ruangrotsakun, Delyar Tabatabai, Melissa Perez, and Minsuk Kahng.
arXiv preprint arXiv:2205.06935, 2022.

Use Your Own Data

In the public deployment, we hosted our data in the DendroMap Data repository. You can use your own data by following the instructions and example in the DendroMap Data README.md and you can use our python functions found in the clustering folder in this repo. There, you will find specific examples and instructions for how to generate the clustering files.

After generating those files, you can add another option in the src/dataOptions.js file as an object to specify how to read your data with the correct format. This is also detailed in the DendroMap Data README.md, and is simple as adding an option like this:

{
	dataset: "YOUR DATASET NAME",
	model: "YOUR MODEL NAME",
	cluster_filepath: "CLUSTER_FILEPATH",
	class_cluster_filepath: "CLASS_CLUSTER_FILEPATH**OPTIONAL**",
	image_filepath: "IMAGE_FILEPATH",
}

in the src/dataOptions.js options array. Paths start from the public folder, so put your data in there. For more information, go to the README.md in the clustering folder. Notebooks that computed the data in DendroMap Data are located there.

DendroMap Component

The DendroMap treemap visualization itself (not the whole project) only relies on having d3.js and the accompanying Javascript files in the src/components/dendroMap directory. You can reuse that Svelte component by importing from src/components/dendroMap/DendroMap.svelte.

The Component is used in src/App.svelte for an example on what props it takes. Here is the rundown of a simple example: at the bare minimum you can create the DendroMap component with these props (propName:type).

<DendroMap
	dendrogramData:dendrogramNode // (root node as nested JSON from dendrogram-data repo)
	imageFilepath:string // relative path from public dir
	imageWidth:number
	imageHeight:number
	width:number
	height:number
	numClustersShowing:number // > 1
/>

A more comprehensive list of props is below, but please look in the src/components/dendroMap/DendroMap.svelte file to see more details: there are many defaults arguments.

<DendroMap
	dendrogramData: dendrogramNode // (root node as nested JSON from dendrogram-data repo)
	imageFilepath: string // relative path from public dir
	imageWidth: number
	imageHeight: number
	width: number
	height: number
	numClustersShowing: number // > 1

	// the very long list of optional props that you can use to customize the DendroMap
	// ? is not in the actual name, just indicates optional
	highlightedOpacity?: number // between [0.0, 1.0]
	hiddenOpacity?: number // between [0.0, 1.0]
	transitionSpeed?: number // milliseconds for the animation of zooming
	clusterColorInterpolateCallback?: (normalized: number) => string // by default uses d3.interpolateGreys
	labelColorCallback?: (d: d3.HierarchyNode) => string
	labelSizeCallback?: (d: d3.HierarchyNode) => string
	misclassificationColor?: string
	outlineStrokeWidth?: string
	outerPadding?: number // the outer perimeter space of a rects
	innerPadding?: number // the touching inside space between rects
	topPadding?: number // additional top padding on the top of rects
	labelYSpace?: number // shifts the image grid down to make room for label on top

	currentParentCluster?: d3.HierarchyNode // this argument is used to bind: for svelte, not really a prop
	// breadth is the default and renders nodes left to right breadth first traversal
	// min_merging_distance is the common way to get dendrogram clusters from a dendrogram
	// max_node_count traverses and splits the next largest sized node, resulting in an even rendering
	renderingMethod?: "breadth" | "min_merging_distance" | "max_node_count" | "custom_sort"
	// this is only in effect if the renderingMethod is "custom_sort". Nodes last are popped and rendered first in the sort
	customSort?: (a: dendrogramNode, b: dendrogramNode) => number // see example in code
	imagesToFocus?: number[] // instance index of the ones to highlight
	outlineMisclassified?: boolean
	focusMisclassified?: boolean
	clusterLabelCallback?: (d: d3.HierarchyNode) => string
	imageTitleCallback?: (d: d3.HierarchyNode) => string

	// will fire based on user interaction
	// detail contains <T> {data: T, element: HTMLElement, event}
	on:imageClick?: ({detail}) => void
	on:imageMouseEnter?: ({detail}) => void
	on:imageMouseLeave?: ({detail}) => void
	on:clusterClick?: ({detail}) => void
	on:clusterMouseEnter?: ({detail}) => void
	on:clusterMouseLeave?: ({detail}) => void
/>

Run Locally!

This project uses Svelte. You can run the code on your local machine by using one of the following: development or build.

Development

cd dendromap      # inside the dendromap directory
npm install       # install packages if you haven't
npm run dev       # live-reloading server on port 8080

then navigate to port 8080 for a live-reloading on file change development server.

Build

cd dendromap		# inside the dendromap directory
npm install       	# install packages if you haven't
npm run build       	# build project
npm run start		# run on port 8080

then navigate to port 8080 for the static build server.

Links

Owner
DIV Lab
Data Interaction and Visualization Lab at Oregon State University
DIV Lab
Machine Learning Models were applied to predict the mass of the brain based on gender, age ranges, and head size.

Brain Weight in Humans Variations of head sizes and brain weights in humans Kaggle dataset obtained from this link by Anubhab Swain. Image obtained fr

Anne Livia 1 Feb 02, 2022
AI-UPV at IberLEF-2021 DETOXIS task: Toxicity Detection in Immigration-Related Web News Comments Using Transformers and Statistical Models

AI-UPV at IberLEF-2021 DETOXIS task: Toxicity Detection in Immigration-Related Web News Comments Using Transformers and Statistical Models Description

Angel de Paula 0 Jun 08, 2022
4st place solution for the PBVS 2022 Multi-modal Aerial View Object Classification Challenge - Track 1 (SAR) at PBVS2022

A Two-Stage Shake-Shake Network for Long-tailed Recognition of SAR Aerial View Objects 4st place solution for the PBVS 2022 Multi-modal Aerial View Ob

LinpengPan 5 Nov 09, 2022
Source code of AAAI 2022 paper "Towards End-to-End Image Compression and Analysis with Transformers".

Towards End-to-End Image Compression and Analysis with Transformers Source code of our AAAI 2022 paper "Towards End-to-End Image Compression and Analy

37 Dec 21, 2022
An OpenAI Gym environment for multi-agent car racing based on Gym's original car racing environment.

Multi-Car Racing Gym Environment This repository contains MultiCarRacing-v0 a multiplayer variant of Gym's original CarRacing-v0 environment. This env

Igor Gilitschenski 56 Nov 01, 2022
Unofficial PyTorch implementation of Attention Free Transformer (AFT) layers by Apple Inc.

aft-pytorch Unofficial PyTorch implementation of Attention Free Transformer's layers by Zhai, et al. [abs, pdf] from Apple Inc. Installation You can i

Rishabh Anand 184 Dec 12, 2022
Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach

Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach This is the implementation of traffic prediction code in DTMP based on PyTo

chenxin 1 Dec 19, 2021
Official PyTorch code for the paper: "Point-Based Modeling of Human Clothing" (ICCV 2021)

Point-Based Modeling of Human Clothing Paper | Project page | Video This is an official PyTorch code repository of the paper "Point-Based Modeling of

Visual Understanding Lab @ Samsung AI Center Moscow 64 Nov 22, 2022
Metric learning algorithms in Python

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

1.3k Dec 28, 2022
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.

Swin Transformer for Semantic Segmentation of satellite images This repo contains the supported code and configuration files to reproduce semantic seg

23 Oct 10, 2022
Robustness via Cross-Domain Ensembles

Robustness via Cross-Domain Ensembles [ICCV 2021, Oral] This repository contains tools for training and evaluating: Pretrained models Demo code Traini

Visual Intelligence & Learning Lab, Swiss Federal Institute of Technology (EPFL) 27 Dec 23, 2022
Code for "Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance" at NeurIPS 2021

Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance Justin Lim, Christina X Ji, Michael Oberst, Saul Blecker, Leor

Sontag Lab 3 Feb 03, 2022
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions

Natural Posterior Network This repository provides the official implementation o

Oliver Borchert 54 Dec 06, 2022
Code base for the paper "Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation"

This repository contains code for the paper Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiati

8 Aug 28, 2022
SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021]

SSD: A Unified Framework for Self-Supervised Outlier Detection [ICLR 2021] Pdf: https://openreview.net/forum?id=v5gjXpmR8J Code for our ICLR 2021 pape

Princeton INSPIRE Research Group 113 Nov 27, 2022
EZ graph is an easy to use AI solution that allows you to make and train your neural networks without a single line of code.

EZ-Graph EZ Graph is a GUI that allows users to make and train neural networks without writing a single line of code. Requirements python 3 pandas num

1 Jul 03, 2022
Graph Neural Networks with Keras and Tensorflow 2.

Welcome to Spektral Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to

Daniele Grattarola 2.2k Jan 08, 2023
Over-the-Air Ensemble Inference with Model Privacy

Over-the-Air Ensemble Inference with Model Privacy This repository contains simulations for our private ensemble inference method. Installation Instal

Selim Firat Yilmaz 1 Jun 29, 2022
DeepFaceLab fork which provides IPython Notebook to use DFL with Google Colab

DFL-Colab — DeepFaceLab fork for Google Colab This project provides you IPython Notebook to use DeepFaceLab with Google Colaboratory. You can create y

779 Jan 05, 2023
This tutorial aims to learn the basics of deep learning by hands, and master the basics through combination of lectures and exercises

2021-Deep-learning This tutorial aims to learn the basics of deep learning by hands, and master the basics through combination of paper and exercises.

108 Feb 24, 2022