Compositional Sketch Search

Last update: Sep 06, 2021

Related tags

Deep Learning compsketch

Overview

Compositional Sketch Search

Official repository for ICIP 2021 Paper: Compositional Sketch Search

Requirements

Install and activate conda environment

conda env create -f env.yml
conda activate compsketch
cd src

If you're having trouble with installation, try changing faiss-gpu to faiss-cpu in env.yml.

Indexing

Either download faiss index for OpenImages or Unsplash indexes here and place them in indexes/ or index your own dataset like so:

python indexing.py --root PathToImages --name IndexName

Searching

Run the streamlit app

streamlit run streamlit_app.py

If you are using a custom dataset:

Pick 'Custom' in the Dataset selection box.
Pick index file in the second selection box.
Paste the same dataset root you used during indexing.

Sketch objects, using one colour per object. Press "Search" to see results!

Troubleshooting

On a Mac, I had to run this command to make it work

export KMP_DUPLICATE_LIB_OK=TRUE

Owner

Alexander Black

PhD student at University of Surrey CVSSP | Adobe Content Authenticity Initiative - Video Content Authenticity

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Compositional Sketch Search

Related tags

Overview

Compositional Sketch Search

Requirements

Indexing

Searching

Troubleshooting

Owner

Alexander Black

.NET bindings for the Pytorch engine

Deep Distributed Control of Port-Hamiltonian Systems

Unsupervised Attributed Multiplex Network Embedding (AAAI 2020)

Ranger deep learning optimizer rewrite to use newest components

MVS2D: Efficient Multi-view Stereo via Attention-Driven 2D Convolutions

SegNet-like Autoencoders in TensorFlow

TeST: Temporal-Stable Thresholding for Semi-supervised Learning

bio_inspired_min_nets_improve_the_performance_and_robustness_of_deep_networks

This is a JAX implementation of Neural Radiance Fields for learning purposes.

Code for the paper One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation, CVPR 2021.

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