mirror of https://github.com/PyRetri/PyRetri.git
82 lines
3.3 KiB
Markdown
82 lines
3.3 KiB
Markdown
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# Installation
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## Requirements
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- Linux (Windows is not officially supported)
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- Python 3.5 or higher
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- PyTorch 1.2.0 or higher
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- torchvison 0.4.0 or higher
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- CUDA 9.0 or higher
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- numpy
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- sklearn
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- yacs
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- tqdm
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Our experiments are conducted on the following environment:
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- PyTorch 1.2.0
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- torchvision 0.4.0
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- CUDA 9.0
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- numpy 1.17.2
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- sklearn 0.21.3
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- tqdm 4.36.1
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## Install PyRetri
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1. Install PyTorch and torchvision following the official instructions.
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2. Clone the PyRetri repository.
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```she
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git clone https://github.com/???
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cd ???
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```
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3. Install PyRetri.
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```shell
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python setup.py install
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```
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## Prepare Datasets
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### Datasets
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In our experiments, we use four general image retrieval dataset and two person re-identification dataset.
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- [Oxford5k](https://www.robots.ox.ac.uk/~vgg/data/oxbuildings/): collecting crawling images from Flickr using the names of 11 different landmarks in Oxford, which stands for landmark recognition task.
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- [CUB-200-2011](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html): containing photos of 200 bird species, which represents fine-grained visual categorization task.
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- [Indoor](http://web.mit.edu/torralba/www/indoor.html): containing indoor scene images with 67 categories, representing scene recognition task.
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- [Caltech101](http://www.vision.caltech.edu/Image_Datasets/Caltech101/): consisting pictures of objects belonging to 101 categories, standing for general object recognition task.
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- [Market-1501](http://www.liangzheng.com.cn/Project/project_reid.html): containing images taken on the Tsinghua campus under 6 camera viewpoints, representing person re-identification task.
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- [DukeMTMC-reID](https://drive.google.com/file/d/1jjE85dRCMOgRtvJ5RQV9-Afs-2_5dY3O/view): containing images captured by 8 cameras, which is more challenging.
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To reproduce our experimental results, you need to first download these datasets, then follow these steps to re-organize the dataset.
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### Split Dataset
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For image retrieval task, the dataset should be divided into two subset: query set and gallery set. If your dataset has been divided already, you can skip this step.
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In order to help you to reproduce our results conventionally, we provide several txt files, each of which is the division protocol used in our experiments. These txt files can be found in [split_file] and you can use the following command to split the dataset mentioned above:
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```shell
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python3 main/split_dataset.py [-d ${dataset}] [-sf ${split_file}]
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```
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Arguments:
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- `dataset`: Path of the dataset to be splitted.
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- `split_file`: Path of the division protocol txt file, with each line corresponding to one image:<image_path> <is_gallery_image>. <image_path> corresponds to the relative path of the image, and a value of 1 or 0 for <is_gallery_image> denotes that the file is in the gallery or query set, respectively.
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Examples:
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```shell
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python3 main/split_dataset.py -d /data/caltech101/ -sf split_file/caltech_split.txt
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```
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Then query folder and gallery folder will be created under the dataset folder.
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Note:
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1. For Re-ID dataset, the images are well divided in advance, so we do not need to split it.
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2. Since we use symlink images instead of copying images to split the dataset, the overwrite operation is prohibited. In other words, if you want to split the dataset again, please remember to delete the last generated folders.
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