CW2 Training Instructions
CW2_training_instructions
CW2_training_instructions
CW2_training_instructions
CW2_training_instructions
CW2_training_instructions
User Manual:
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Instructions for training/testing of your designs:
Dataset:
CUHK03 is a very popular dataset containing 13,164 images of 1,360 pedestrians. Sample images
from this dataset are presented below:
For the purpose of this coursework you are provided with both images from the dataset and feature
vectors extracted from the images. Images in this dataset were captured with use of two cameras,
what is more, each person was photographed a number of times. Therefore, each identity (person) is
represented in the dataset approximately 8-9 times. All the data you need is contained in
CW2_data.zip. Please unpack it, and you should find the following contents:
1. Folder images_cuhk03 contains all the images from the dataset. Their names may seem illogical
at first, but they are properly annotated in the file cuhk03_new_protocol_config_labeled.mat.
2. Matlab file cuhk03_new_protocol_config_labeled.mat. Once you open this file with Matlab or
Python, you will find 6 main components (in the form of arrays):
a. camId specifies whether image was taken from camera 1 or camera 2. While evaluating
(testing) your algorithms, you should not consider images of your current query identity taken from
the same camera. For example, when you create ranklist for the first query image (index 22, label 3,
camera 1, name "1_003_1_02.png"), you should not include images with indexes 21, 23, 24 in this
ranking list, as these are images of the same person (label 3) captured by the camera with index 1.
b. filelist specifies correspondences between names of files in images_cuhk03 and their
indexes.
c. gallery_idx, which specifies indexes of the part of the dataset from which you compose
your ranklists during testing phase
d. labels contains ground truths for each image
e. query_idx contains indexes of query images
f. train_idx contains indexes of images that can be used for training and validation
To load cuhk03_new_protocol_config_labeled.mat into Python you can use the code from this
example (and repeat for other arrays listed above):
from scipy.io import loadmat
train_idxs = loadmat('cuhk03_new_protocol_config_labeled.mat')
['train_idx'].flatten()
with Matlab you can simply use load('cuhk03_new_protocol_config_labeled.mat') or double click
on the file in File Explorer.
Apart from two files mentioned above, file feature_data.json contains an array 14096x2048 with
some feature vectors representing each image in the dataset. YOU SHOULD PERFORM YOUR
COMPUTATIONS WITH USE OF FEATURE VECTORS PROVIDED, IMAGES ARE
THERE ONLY FOR THE REFERENCE AND VISUALISATION. With Pyhon you can simply
load it as follows:
import json
with open('feature_data.json', 'r') as f:
features = json.load(f)
and with Matlab:
features = jsondecode(fileread('feature_data.json'));
Please note it takes a while to load a file as it contains considerably large amount of data.
Training, validation, testing:
1. Training:
For the purpose of training use feature vectors provided in the file feature_data.json. Indexes of
vectors that can be used for training are stored in the train_idx vector belonging to
cuhk03_new_protocol_config_labeled.mat.
2. Validation:
You can use 100 randomly selected identities from training set as validation set. Please note each
identity is represented in the dataset 7-10 times (there is a number of photos/feature vectors of each
person in the dataset), and you should include all of them in the validation set, therefore around
700-1000 different feature vectors will be used for validation. There exists an idea in Computer
Vision that you use validation set only to specify the number of iterations that is optimal for your
design and then you include your validation set into train set and perform final training (without
validation set) for this fixed amount of iterations. You can try this idea as well. Once again, use
feature vectors provided in the file feature_data.json. Indexes of vectors that can be used for
training/validation are stored in the train_idx.
3. Testing:
Finally, you perform testing. Both query_idx and gallery_idx specify your test set. The task is to
take all the feature vectors from query set, one by one, and create a ranklist for each of them, by
finding nearest neighbours within gallery_idx, deleting images/feature vectors of considered query
identity, captured by the same camera (same label and same camId). Sample ranklists after
deletion, with use of dataset images for visualisation (green frame indicates positive matches, red
are negative ones, black are query images, you can see positive matches presenting different views
of the query identity, due to deletion mentioned above):
Once having your ranklists you can calculate @rank1, etc. scores for such ranklists, achievable
scores for simple nearest neighbour and Euclidean distance are:
top1:0.469286 top5:0.668571 top10:0.750000 mAP:0.432923 (I managed to do a bit better with
simple NN on Matlab - @rank1 = 0.47, so don’t worry if you achieve a bit better scores).
Please do not mix sub-datasets:
train_idx: training, validation (optional)
query_idx + gallery_idx: only for testing your design, follow the procedure above
References:
W. Li, R. Zhao, T. Xiao, and X. Wang, “Deepreid: Deep filter pairing neural network for person reidentification,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, June 2014,
pp. 152–159.
Z. Zhong, L. Zheng, D. Cao, and S. Li, “Re-ranking person re-identification with k-reciprocal
encoding,” CoRR, vol. abs/1701.08398, 2017. [Online]. Available: http://arxiv.org/abs/1701.08398
CUHK03 dataset:
http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html
https://github.com/zhunzhong07/person-re-ranking/tree/master/CUHK03-NP
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