https://github.com/Andrew-Brown1/Smooth_AP

code for the ECCV '20 paper "Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval"
https://github.com/Andrew-Brown1/Smooth_AP

Last synced: about 2 months ago
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code for the ECCV '20 paper "Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval"

• Host: GitHub
• URL: https://github.com/Andrew-Brown1/Smooth_AP
• Owner: Andrew-Brown1
• License: mit
• Created: 2020-07-14T13:31:55.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2021-04-01T15:23:27.000Z (over 3 years ago)
• Last Synced: 2024-07-10T09:52:05.561Z (3 months ago)
• Language: Python
• Size: 6.19 MB
• Stars: 198
• Watchers: 11
• Forks: 32
• Open Issues: 9
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# Smooth_AP

code for the ECCV '20 paper ["Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval"](https://www.robots.ox.ac.uk/~vgg/research/smooth-ap/)

The PyTorch implementation of the Smooth-AP loss function is found in src/Smooth_AP_loss.py

Training code and pre-trained weights coming soon...

![teaser](https://github.com/Andrew-Brown1/Smooth_AP/blob/master/ims/teaser.png)

## Dependencies

- Python 3.7.7

- PyTorch 1.6.0

- Cuda 10.1

## Data

This repository is used for training using Smooth-AP loss on the following datasets:

- PKU Vehicle ID (obtained from this website https://pkuml.org/resources/pku-vehicleid.html - must email authors for download permission)

- INaturalist (2018 version - obtained from this website https://www.kaggle.com/c/inaturalist-2018/data)

We are the first to use the large-scale INaturalist dataset for the task of image retreival. The dataset splits can be downloaded here: https://drive.google.com/file/d/1sXfkBTFDrRU3__-NUs1qBP3sf_0uMB98/view?usp=sharing . Unpack the zip into the INaturalist dataset directory. 

## Training the model

training results for the Vehicle ID and Inaturalist datasets can be replicated using this repository. To train the model on the Vehicle ID dataset, you can run: 

- python main.py --fc_lr_mul 1 --bs 384

## Paper

If you find this work useful, please consider citing:

```

@InProceedings{Brown20,

  author       = "Andrew Brown and Weidi Xie and Vicky Kalogeiton and Andrew Zisserman ",

  title        = "Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval",

  booktitle    = "European Conference on Computer Vision (ECCV), 2020.",

  year         = "2020",

}

```