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
Deep Learning Visuals contains 215 unique images divided in 23 categories

Deep Learning Visuals contains 215 unique images divided in 23 categories (some images may appear in more than one category). All the images were originally published in my book "Deep Learning with P

Daniel Voigt Godoy 1.3k Dec 28, 2022
official implemntation for "Contrastive Learning with Stronger Augmentations"

CLSA CLSA is a self-supervised learning methods which focused on the pattern learning from strong augmentations. Copyright (C) 2020 Xiao Wang, Guo-Jun

Lab for MAchine Perception and LEarning (MAPLE) 47 Nov 29, 2022
Image Segmentation using U-Net, U-Net with skip connections and M-Net architectures

Brain-Image-Segmentation Segmentation of brain tissues in MRI image has a number of applications in diagnosis, surgical planning, and treatment of bra

Angad Bajwa 8 Oct 27, 2022
Random Erasing Data Augmentation. Experiments on CIFAR10, CIFAR100 and Fashion-MNIST

Random Erasing Data Augmentation =============================================================== black white random This code has the source code for

Zhun Zhong 654 Dec 26, 2022
FIRM-AFL is the first high-throughput greybox fuzzer for IoT firmware.

FIRM-AFL FIRM-AFL is the first high-throughput greybox fuzzer for IoT firmware. FIRM-AFL addresses two fundamental problems in IoT fuzzing. First, it

356 Dec 23, 2022
A library for building and serving multi-node distributed faiss indices.

About Distributed faiss index service. A lightweight library that lets you work with FAISS indexes which don't fit into a single server memory. It fol

Meta Research 170 Dec 30, 2022
Official implementation of NeurIPS'2021 paper TransformerFusion

TransformerFusion: Monocular RGB Scene Reconstruction using Transformers Project Page | Paper | Video TransformerFusion: Monocular RGB Scene Reconstru

Aljaz Bozic 118 Dec 25, 2022
🕺Full body detection and tracking

Pose-Detection 🤔 Overview Human pose estimation from video plays a critical role in various applications such as quantifying physical exercises, sign

Abbas Ataei 20 Nov 21, 2022
A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval

CLIP4CMR A Comprehensive Empirical Study of Vision-Language Pre-trained Model for Supervised Cross-Modal Retrieval The original data and pre-calculate

24 Dec 26, 2022
Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D

Hannes Stärk 95 Dec 30, 2022
Robocop is your personal mini voice assistant made using Python.

Robocop-VoiceAssistant To use this project, you should have python installed in your system. If you don't have python installed, install it beforehand

Sohil Khanduja 3 Feb 26, 2022
Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

CoProtector Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

Zhensu Sun 1 Oct 26, 2021
The 1st Place Solution of the Facebook AI Image Similarity Challenge (ISC21) : Descriptor Track.

ISC21-Descriptor-Track-1st The 1st Place Solution of the Facebook AI Image Similarity Challenge (ISC21) : Descriptor Track. You can check our solution

lyakaap 75 Jan 08, 2023
Learning Logic Rules for Document-Level Relation Extraction

LogiRE Learning Logic Rules for Document-Level Relation Extraction We propose to introduce logic rules to tackle the challenges of doc-level RE. Equip

41 Dec 26, 2022
Generating Radiology Reports via Memory-driven Transformer

R2Gen This is the implementation of Generating Radiology Reports via Memory-driven Transformer at EMNLP-2020. Citations If you use or extend our work,

CUHK-SZ NLP Group 101 Dec 13, 2022
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN.

Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU.

Phil Wang 2.3k Jan 09, 2023
Unofficial Implementation of MLP-Mixer in TensorFlow

mlp-mixer-tf Unofficial Implementation of MLP-Mixer [abs, pdf] in TensorFlow. Note: This project may have some bugs in it. I'm still learning how to i

Rishabh Anand 24 Mar 23, 2022
YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )

Yolo v4, v3 and v2 for Windows and Linux (neural networks for object detection) Paper YOLO v4: https://arxiv.org/abs/2004.10934 Paper Scaled YOLO v4:

Alexey 20.2k Jan 09, 2023
Improving Machine Translation Systems via Isotopic Replacement

CAT (Improving Machine Translation Systems via Isotopic Replacement) Machine translation plays an essential role in people’s daily international commu

Zeyu Sun 10 Nov 30, 2022
Fang Zhonghao 13 Nov 19, 2022