Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval.

Last update: Jan 9, 2022

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Overview

DARP-SBIR

Intro

This repository contains the source code implementation for ICDM submission paper Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval. The python files SBIR_*.py are the core framework codes to run this project under different settings, by integrating training code and evaluation code all in one file.

Dependencies

numpy
pickle
pytorch>=1.5.0
torchvision>=0.7.0
tqdm
tensorboard
bresenham
Pillow

Pre-trained

Pre-trained Embeddings
- Train.pickle
- Test.pickle
Pre-trained Models
- In folder ckpt

Results and Commands

To produce such results, run the corresponding entrance main files.

Triplet Network: main_pure.py
Triplet + Vanilla RL: main_RL.py
Triplet + PPO: main_finetune.py
Bootstrapped DQN: main_dqn.py
DARP-SBIR: main_boot.py

For the other two datasets, use main_shoev2.py and main_sketchy.py

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