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https://github.com/Confusezius/ICCV2019_MIC
(ICCV 2019) This repo contains code for "MIC: Mining Interclass Characteristics for Improved Metric Learning", which proposes an auxiliary training task to explain away intra-class variations.
https://github.com/Confusezius/ICCV2019_MIC
cars196 computer-vi cub200 deep-lear deep-metric-learning metric- pku-vehicle pytorch shop-clothes-retrieval
Last synced: 3 months ago
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(ICCV 2019) This repo contains code for "MIC: Mining Interclass Characteristics for Improved Metric Learning", which proposes an auxiliary training task to explain away intra-class variations.
- Host: GitHub
- URL: https://github.com/Confusezius/ICCV2019_MIC
- Owner: Confusezius
- Created: 2019-07-30T09:07:11.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-06-12T20:42:46.000Z (over 4 years ago)
- Last Synced: 2024-05-21T15:44:00.636Z (6 months ago)
- Topics: cars196, computer-vi, cub200, deep-lear, deep-metric-learning, metric-, pku-vehicle, pytorch, shop-clothes-retrieval
- Language: Python
- Homepage: https://confusezius.github.io/metric-learning-mining-interclass-characteristics/
- Size: 15 MB
- Stars: 60
- Watchers: 7
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# MIC: Mining Interclass Characteristics for Improved Deep Metric Learning
---
#### ___Authors___:
* Karsten Roth ([email protected])
* Biagio Brattoli ([email protected])
* Björn Ommer
___Primary Contact___: Karsten Roth
For baseline implementations, check out https://github.com/Confusezius/Revisiting_Deep_Metric_Learning_PyTorch and the accompanying paper!
---
This repository contains the code to run the pipeline proposed in our ICCV 2019 paper _Mining Interclass Characteristics for Improved Deep Metric Learning_ (https://arxiv.org/abs/1909.11574). The results using this pipeline for ProxyNCA and Triplet with Semihard Sampling are better than noted in the paper due to an improved implementation of the baseline methods.
**Note:** Baseline implementations can be found at https://github.com/Confusezius/Deep-Metric-Learning-Baselines.
---
### Requirements
Our method was tested around
* Python Version 3.6.6+
* PyTorch Version 1.0.1+ and Cuda 8.0
* Faiss(-gpu) 1.5.1 (GPU support optional) for Cuda 8.0
* Scikit Image 0.14.2
* Scikit Learn 0.20.3
* Scipy 1.2.1
To run with standard batch sizes, at least 11 GB of VRAM is required (e.g. 1080Ti, Titan X).
---
### How To Use
For a quick start for standard Deep Metric Learning datasets:
* [CUB200-2011](http://www.vision.caltech.edu/visipedia/CUB-200.html)
* [CARS196](https://ai.stanford.edu/~jkrause/cars/car_dataset.html)
* [Stanford Online Products](http://cvgl.stanford.edu/projects/lifted_struct/)
* [In-Shop Clothes](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/InShopRetrieval.html)
* [PKU Vehicle-ID](https://www.pkuml.org/resources/pku-vds.html)
simply run the sample setups given in `Result_Runs.sh`. These give similar values (*assuming the underlying setup to be the same*) as those reported in the paper. Minor differences are due to choice of seeds and underlying setups.
---
### Specifics
The main script is `main.py`. Running it with default flags will provide a Metric Learning Run with Interclass Mining on CUB200-2011 using Resnet50, Marginloss and Distance-weighted Sampling. For all tweakable parameters and their purpose, please refer to the help-strings in the `main.py`-ArgumentParser. Most should be fairly self-explanatory. Again, good default setups can be found in `Result_Runs.sh`.
__NOTE__ regarding ProxyNCA for __Online Products__, __PKU Vehicle ID__ and __In-Shop Clothes__: Due to the high number of classes, the number of proxies required is too high for useful training (>10000 proxies).
---
### Repo Structure
```
Repository
│ README.md
|
| ### Main Scripts
| main.py (main training script)
| losses.py (collection of loss and sampling impl.)
│ datasets.py (dataloaders for all datasets)
│
│ ### Utility scripts
| auxiliaries.py (set of utilities)
| evaluate.py (set of evaluation functions)
│
│ ### Network Scripts
| netlib.py (contains impl. for ResNet50 and network utils)
| googlenet.py (contains impl. for GoogLeNet)
│
└───Training Results (generated during Training)
| │ e.g. cub200/Training_Run_Name
| │ e.g. cars196/Training_Run_Name
│
└───Datasets (should be added, if one does not want to set paths)
| │ cub200, cars196 ...
```
---
### Required Dataset Structures
__CUB200-2011__
```
cub200
└───images
| └───001.Black_footed_Albatross
| │ Black_Footed_Albatross_0001_796111
| │ ...
| ...
```
__CARS196__
```
cars196
└───images
| └───Acura Integra Type R 2001
| │ 00128.jpg
| │ ...
| ...
```
__Online Products__
```
online_products
└───images
| └───bicycle_final
| │ 111085122871_0.jpg
| ...
└───Info_Files
| │ bicycle.txt
| │ ...
```
__In-Shop Clothes__
```
in-shop
└───img
| └───MEN
| └───Denim
| └───id_00000080
| │ 01_1_front.jpg
| │ ...
| ...
└───Eval
| │ list_eval_partition.txt
```
__PKU Vehicle ID__
```
vehicle_id
└───image
| │ ![]()
.jpg
| | ...
└───train_test_split
| | test_list_800.txt
| | ...
```
---
### Stored Data:
By default, the following files are saved:
```
Name_of_Training_Run
| checkpoint.pth.tar -> Contains network state-dict.
| hypa.pkl -> Contains all network parameters as pickle.
| Can be used directly to recreate the network.
| log_train_Class.csv -> Logged training data as CSV.
| log_val_Class.csv -> Logged test metrics as CSV.
| Parameter_Info.txt -> All Parameters stored as readable text-file.
| InfoPlot_Class.svg -> Graphical summary of training/testing metrics progression.
| Curr_Summary_Class.txt -> Summary of training (best metrics...).
| sample_recoveries.png -> Sample recoveries for best validation weights.
| Acts as a sanity test.
```
---
## Citing Our Paper
If you use this repository or wish to cite our results, please use (https://arxiv.org/abs/1909.11574)
```
@conference{roth2019mic,
title={MIC: Mining Interclass Characteristics for Improved Metric Learning},
author={Roth, Karsten, and Brattoli, Biagio, and Ommer, Bj\"orn},
booktitle={Proceedings of the International Conference on Computer Vision (ICCV)},
year={2019}
}
```