Kaiyang
GitHub
commit
6e498f8b17
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@ -33,6 +33,7 @@ You can find some research projects that are built on top of Torchreid `here <ht
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What's new
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------------
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- [Feb 2021] We support the new multi-view multi-source geo-localization dataset `University-1652 <https://dl.acm.org/doi/abs/10.1145/3394171.3413896>`_.
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- [Feb 2021] ``v1.3.5``: Now the `cython code <https://github.com/KaiyangZhou/deep-person-reid/pull/412>`_ works on Windows (credit to `lablabla <https://github.com/lablabla>`_).
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- [Jan 2021] Our recent work, `MixStyle <https://openreview.net/forum?id=6xHJ37MVxxp>`_ (mixing instance-level feature statistics of samples of different domains for improving domain generalization), has been accepted to ICLR'21. The code has been released at https://github.com/KaiyangZhou/mixstyle-release where the person re-ID part is based on Torchreid.
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- [Jan 2021] A new evaluation metric called `mean Inverse Negative Penalty (mINP)` for person re-ID has been introduced in `Deep Learning for Person Re-identification: A Survey and Outlook (TPAMI 2021) <https://arxiv.org/abs/2001.04193>`_. Their code can be accessed at `<https://github.com/mangye16/ReID-Survey>`_.
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@ -230,6 +231,10 @@ Image-reid datasets
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- `QMUL-iLIDS <http://www.eecs.qmul.ac.uk/~sgg/papers/ZhengGongXiang_BMVC09.pdf>`_
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- `PRID <https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf>`_
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Geo-localization datasets
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^^^^^^^^^^^^^^^^^^^^^^^
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- `University-1652 <https://dl.acm.org/doi/abs/10.1145/3394171.3413896>`_
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Video-reid datasets
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^^^^^^^^^^^^^^^^^^^^^^^
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- `MARS <http://www.liangzheng.org/1320.pdf>`_
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@ -2,7 +2,7 @@ from __future__ import print_function, absolute_import
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from .image import (
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GRID, PRID, CUHK01, CUHK02, CUHK03, MSMT17, VIPeR, SenseReID, Market1501,
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DukeMTMCreID, iLIDS
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DukeMTMCreID, iLIDS, University1652
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)
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from .video import PRID2011, Mars, DukeMTMCVidReID, iLIDSVID
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from .dataset import Dataset, ImageDataset, VideoDataset
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@ -18,7 +18,8 @@ __image_datasets = {
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'ilids': iLIDS,
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'sensereid': SenseReID,
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'prid': PRID,
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'cuhk02': CUHK02
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'cuhk02': CUHK02,
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'university1652':University1652
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}
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__video_datasets = {
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@ -11,3 +11,4 @@ from .msmt17 import MSMT17
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from .sensereid import SenseReID
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from .market1501 import Market1501
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from .dukemtmcreid import DukeMTMCreID
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from .university1652 import University1652
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@ -0,0 +1,102 @@
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from __future__ import division, print_function, absolute_import
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import re
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import glob
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import os.path as osp
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import os
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import gdown
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from ..dataset import ImageDataset
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class University1652(ImageDataset):
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"""University-1652.
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Reference:
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- Zheng et al. University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization. ACM MM 2020.
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URL: `<https://github.com/layumi/University1652-Baseline>`_
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OneDrive:
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https://studentutsedu-my.sharepoint.com/:u:/g/personal/12639605_student_uts_edu_au/Ecrz6xK-PcdCjFdpNb0T0s8B_9J5ynaUy3q63_XumjJyrA?e=z4hpcz
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[Backup] GoogleDrive:
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https://drive.google.com/file/d/1iVnP4gjw-iHXa0KerZQ1IfIO0i1jADsR/view?usp=sharing
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[Backup] Baidu Yun:
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https://pan.baidu.com/s/1H_wBnWwikKbaBY1pMPjoqQ password: hrqp
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Dataset statistics:
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- buildings: 1652 (train + query).
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- The dataset split is as follows:
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| Split | #imgs | #buildings | #universities|
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| -------- | ----- | ----| ----|
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| Training | 50,218 | 701 | 33 |
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| Query_drone | 37,855 | 701 | 39 |
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| Query_satellite | 701 | 701 | 39|
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| Query_ground | 2,579 | 701 | 39|
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| Gallery_drone | 51,355 | 951 | 39|
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| Gallery_satellite | 951 | 951 | 39|
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| Gallery_ground | 2,921 | 793 | 39|
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- cameras: None.
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datamanager = torchreid.data.ImageDataManager(
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root='reid-data',
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sources='university1652',
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targets='university1652',
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height=256,
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width=256,
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batch_size_train=32,
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batch_size_test=100,
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transforms=['random_flip', 'random_crop']
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)
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"""
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dataset_dir = 'university1652'
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dataset_url = 'https://drive.google.com/uc?id=1iVnP4gjw-iHXa0KerZQ1IfIO0i1jADsR'
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def __init__(self, root='', **kwargs):
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self.root = osp.abspath(osp.expanduser(root))
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self.dataset_dir = osp.join(self.root, self.dataset_dir)
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print(self.dataset_dir)
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if not os.path.isdir(self.dataset_dir):
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os.mkdir(self.dataset_dir)
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gdown.download(self.dataset_url, self.dataset_dir+'data.zip', quiet=False)
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os.system('unzip %s'%(self.dataset_dir+'data.zip'))
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self.train_dir = osp.join(
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self.dataset_dir,'University-Release/train/'
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)
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self.query_dir = osp.join(self.dataset_dir, 'University-Release/test/query_drone')
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self.gallery_dir = osp.join(
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self.dataset_dir, 'University-Release/test/gallery_satellite'
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)
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required_files = [
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self.dataset_dir, self.train_dir, self.query_dir, self.gallery_dir
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]
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self.check_before_run(required_files)
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self.fake_camid = 0
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train = self.process_dir(self.train_dir, relabel=True, train=True)
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query = self.process_dir(self.query_dir, relabel=False)
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gallery = self.process_dir(self.gallery_dir, relabel=False)
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super(University1652, self).__init__(train, query, gallery, **kwargs)
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def process_dir(self, dir_path, relabel=False, train=False):
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IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp')
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if train:
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img_paths = glob.glob(osp.join(dir_path, '*/*/*'))
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else:
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img_paths = glob.glob(osp.join(dir_path, '*/*'))
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pid_container = set()
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for img_path in img_paths:
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if not img_path.lower().endswith(IMG_EXTENSIONS):
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continue
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pid = int(os.path.basename(os.path.dirname(img_path)))
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pid_container.add(pid)
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pid2label = {pid: label for label, pid in enumerate(pid_container)}
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data = []
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# no camera for university
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for img_path in img_paths:
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if not img_path.lower().endswith(IMG_EXTENSIONS):
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continue
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pid = int(os.path.basename(os.path.dirname(img_path)))
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if relabel:
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pid = pid2label[pid]
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data.append((img_path, pid, self.fake_camid))
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self.fake_camid +=1
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return data
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