fashionpedia's People
Forkers
kurhula chaoso bhavesh2799 deeplearning2012 pradiptapradhanang andrej1a michelemassaro17 fj-morales dacov albin-jo hwijune duckheada shotantan alec-demoraesfashionpedia's Issues
Question about attribute annotations
Great dataset !
I have some questioins regarding the attribute annotations. I'm wondering if the attributes is related to the categories. For example, 'classic military (jacket)' should be a subcategory for 'jacket'. But there are attributes like 'hoodie', 'blazer' which don't have any information about how they are related to the categories. This makes it hard to filter the attribute prediction with the classification result of the category and determine which categories are mutual exclusive.
Also, it's there a way to get a complete version of this image.
Any help to solve the above problem will be appreciated.
Question about annotation files
I downloaded the instances_attributes_train2020.json and instances_attributes_val2020.json. And I found weird segmentation format . You can search "counts" in the file. Such as these rows:
"segmentation": {
"size": [
1024,
688
],
"counts": "emX8;do02M3M4K4L4L3M4M2M3M4L3M4L3N2B?L3M3]LAhVO[NKU2eh00UWOaM3o1\\g0S2WXOUL7j1\\g0j51O001O1O1O001O1N2O0O2O001N101O0O2O0O1O1O1O1O1O1YOgWOfIZh0U6oWOfIRh0X6UXObIlg0]6[XO\\Ifg0b6m0O1N2K5J6\\O]VOeJii0V5c0M3[ObUOdKbj0Z4c0N2N2M3N2J6H8H8I7E;E;E;E;E<G8J6K5J7I7I
0C[Yh:"
},
Did the file have a problem? Hoping for help.
Remove Background of Image
Hi, so i have been playing with instance segmentation for the past week and cam around this library called Pixel lib
https://pixellib.readthedocs.io/en/latest/Image_instance.html
which is amazing at Instance Segmentation with few lines of Code.
Now it works on People,animals but has a problem on Fashion (Especially Shoes Dataset)
So, I Finally found the Fashion Pedia dataset and annotations but how can i use it with Pixel lib ?
My Code on Kaggle:
pip install pixellib
import pixellib
from pixellib.instance import instance_segmentation
from matplotlib import pyplot as plt
!wget "https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5"
segmentation_model = instance_segmentation()
segmentation_model.load_model("./mask_rcnn_coco.h5")
segmask,output = segmentation_model.segmentImage("../input/facess/8.jpg", extract_segmented_objects = True, save_extracted_objects = True, show_bboxes = True, output_image_name = "output1.jpg")
plt.imshow(Image.open("./segmented_object_1.jpg"))
https://i.postimg.cc/BbnzTW4j/Screenshot-2021-10-26-at-18-53-53-notebook7bd3db2b2e-Kaggle.png
The below image i want to do image segmentation ? how can i do that ?
https://i.postimg.cc/bNGCp9Jf/116.jpg
Thanks
Semantic Segmentation
Hello, happy new year, and thank you for this work!
I was wondering if you had any results for semantic segmentation on this dataset or are aware of any works that have such results?
I appreciate your time.
Thank you!
Josh
What's the difference between this dataset and imaterialist-fashion-2020-fgvc7?
I found this dataset is similar with kaggle competition imaterialist-fashion-2020-fgvc7. The pictures of the two datasets are similar, but the resolution is different, and the label format is different. Can you elaborate on the relationship between these two data sets?
How to use my custom data ?
Hi, everyone.. so i have many pics of footwear.
i want to do instance segmentation of my footwear and remove the background.
how can use fashionpedias API with my custom dateset ? or do i have to annotate them one by one
Does "instances_attributes_val2020" annotation has wrong ids?
Annotation for "attributes" in "instances_attributes_val2020" json looks something like,
'attributes': [{'id': 234, 'name': 'no opening', 'supercategory': 'opening type', 'level': 1, 'taxonomy_id': 'att000242_00'}, {'id': 281, 'name': 'plastic', 'supercategory': 'non-textile material type', 'level': 1, 'taxonomy_id': 'att000298_00'},........, {'id': 340, 'name': 'plant', 'supercategory': 'textile pattern', 'level': 1, 'taxonomy_id': 'att000363_00'}]
there is a jump in ID from 234 to 281 and it ends on 340.
I am assuming there are some wrong annotations, correct me if I am wrong.
if these annotations are correct, then how one can inference attribute from category? where to get relation between category
and attribute
?
In case of inferencing done through fashionpedia detection, How one can extract attribute values from the saved check point?
Some grayscale images in train set?
Hi, thanks for your great work.
I found there are many gray-scale images in the training set. Is this expected?
like ac7f353fd7481926e8fa9eafed609948.jpg
, 7d956f0919f2eab63e481e9321ce2028.jpg
and so on
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