https://github.com/jdhao/deep_firearm
This repo is used to host our code for the work of deep firearm image retrieval.
https://github.com/jdhao/deep_firearm
double-margin-contrastive-loss image-retrieval pytorch
Last synced: about 1 year ago
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This repo is used to host our code for the work of deep firearm image retrieval.
- Host: GitHub
- URL: https://github.com/jdhao/deep_firearm
- Owner: jdhao
- Created: 2018-06-04T07:15:29.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2020-03-31T15:58:43.000Z (over 6 years ago)
- Last Synced: 2025-06-27T15:11:40.481Z (about 1 year ago)
- Topics: double-margin-contrastive-loss, image-retrieval, pytorch
- Language: Python
- Homepage:
- Size: 17.6 KB
- Stars: 11
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Introduction
This is the code our ICPR2018 paper "[DeepFirearm: Learning Discriminative
Feature Representation for Fine-grained Firearm
Retrieval](https://arxiv.org/abs/1806.02984)".
# How to run the code
## Version info
The code is written using the PyTorch version 0.3.0. So In order to run this
code, you may install version 0.3.0 of PyTorch or adapt it to the newer version
of PyTorch.
## Instructions for downloading dataset
You can download the dataset from
[here](https://drive.google.com/drive/folders/1BucERZl51nB20ssxGsQrcT6l59MNLkf7).
Two separate data are used for the experiment. One is for classification
training, and the other is for the retrieval training. After downloading this
dataset, extract it under the folder `data` using the following commands:
```bash
tar -zxvf firearm-train-val.tar.gz -C data/ # for the classification data
```
and
```bash
tar -zxvf firearm-dataset.tar.gz -C data/ # for the retrieval data
```
## Train the classification model
In order to train the classification model, run the following command:
```bash
python train_cls.py
```
## Train the retrieval model after classification
To get better retrieval performance, we further fine-tune the model using
retrieval task based on the classification model. To train the model, use the
following command:
```bash
python train_retr_from_cls.py
```
## Benchmark on test set
To check the model's performance on test set, run the following command:
```python
python benchmark_on_test.py
```
It will show both the mAP and rank-k accuracy for different feature dimensions.
# Citation information
If you use this dataset or use our code, please cite the following work:
```
@INPROCEEDINGS{HJD2018DFLD,
author={J. Hao and J. Dong and W. Wang and T. Tan},
booktitle={2018 24th International Conference on Pattern Recognition (ICPR)},
title={DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval},
year={2018},
volume={},
number={},
pages={3335-3340},
keywords={feature extraction;feedforward neural nets;image classification;image representation;image retrieval;learning (artificial intelligence);convolutional neural networks;single margin contrastive loss;firearm images;double margin contrastive loss;negative image pairs;positive image pairs;fine-grained recognition;Firearm 14k;image retrieval techniques;social media;fine-grained Firearm retrieval;discriminative feature representation;Training;Task analysis;Labeling;Correlation;Image retrieval;Forensics;Convolutional neural networks},
doi={10.1109/ICPR.2018.8545529},
ISSN={1051-4651},
month={Aug},}
```