vana77 / market-1501_attribute Goto Github PK

How to sort the images to match the images and the labels?

for example, the image:

"0665_c6s2_041293_01.jpg"

1. we convert the string "0665" to integer 665, than sort them or
2. we sort the the string "0665" in alphabet order.

In 1. "99" < "100" but In 2 "99" > "100".

I want to know which one the author used ?

What is the license under which the dataset is released?

I have tried loading the annotations in matlab and have not been able to convert them to csv, can you provide this format? thanks

Thanks for the work! I came across the original paper and it seems that only 24 attributes are used in the APR paper, but the dataset has 27 attributes in total. I tried to look through the paper and see if there's any specific reasons for it and did not managed to find any. May I kindly check what are the 3 attributes that are not used and if I am supposed to throw out those attributes when doing experiments?

Thank you!

how to download the dataset @vana77

Hi,
Based on Market-1501 dataset, we have 750 identities for test and 19,732 test images.
I understand the 12 column values, but why 13115 entries?
Thank you

I loaded gallery_market.mat and looked at the values in the third column.

The README.md says that the third column contains values for "Sleeve length" (up) and that possible values are ["long sleeve (1)", "short sleeve (2)"] but some of the data has a value of 3.

>> load gallery_market.mat
>> sum(gallery(:,3)==3)

ans =

        1234

Am I interpreting the data correctly?

Is this the right ordering of attributes?
['gender', 'hair', 'up', 'down', 'clothes', 'hat', 'backpack', 'bag', 'handbag', 'age', 'upcolor', 'downcolor']

Thanks

As shown in following picture, the label indicates that the person doesn't have a bag. But it seems that the person has a bag.
The age seems like adult instead of teenager.
The upper-body clothing seems like pink instead of white.

Hello,
Thank you for your effort,
Could I know how you've annotate this dataset?

Do you apply yolo to crop all the people from any scene then save them as separate images and use a txt file to annotate them ?
or you've used a specific annotation tool for labeling ?

Regards

I have download the dataset and the attribute.mat. but i can't find the train id and test id.

Hi @vana77 , thanks for your wonderful work！

Since the MARS dataset is an extension of the Market-1501, does that mean your attribute notations also work for MARS dataset?

I found two images were inappropriately labeled. The color differences are calculated by CIEDE2000. RGB value standards are according to Wikipedia. I randomly picked a 3x3 area and got the mean RGB value followed by converting to L* A* B* space.

RGB_Pink = [255, 192, 203];
RGB_Purple = [128, 0, 128];
RGB_Yellow = [255, 255, 0];

1. 0430 in bounding_box_train or 227 in market_attribute.train

It was labeled as downpink = 2.
My results: de_Pink = 65.4, de_Purple = 16.3;

2. 0601 in bounding_box_test or 289 in market_attribute.test

It was labeled as upyellow = 2.
My results: de_Yellow = 85.1, de_Purple = 17.8;

Values were different according to the picked area, but the differences were significantly large.

Thanks a lot, i want to label some data with attributes ,but i can't find propriate label tools to finish it.

@vana77 ,Hi,I can not understand the query folder,gt_query folder and gr_bbox,those for what ?Thank you! and forget my poor English.

我修改了作者的matlab代码，将图片地址与标签的对应关系存储成txt文件。 需要的可以下载：
百度网盘：
链接：https://pan.baidu.com/s/1KU8gRgQ6aaHPMXEPt6qcqQ 密码：5wkf

Dear sir, How can I add attribute detection to the re-ID code, using the python language instead of running in matlab

Hi, thank you for sharing the dataset. I'm trying to implement your paper in pytorch. Got some few questions.
Let's suppose I'm using a 64 minibatch on Market-1501 dataset. And I use the 28 attributes (I personally have 30).
Using this section,

number of attribute m = 28
Equation 3). Is simply a Linear layer (ax + b) with bias term follow by a sigmoid.
for 64 minibatch, 
The attribute prediction score has shape [64, 28] i.e., R^{64xm}
The global image representation has shape [64, 2048]

In your paper, you said, you element-wise multiplied the two. How can you multiply the attribute prediction score ([64, 28]) with a feature representation of shape [64, 2048].
can you explain what I'm missing here?
Thank you.

In the mat file, the market_attribute train shape is 751x27.
How can I know the first row of it is which image ?
The bounding box of train folder have 751 identity, but i do not know how to map the label to image