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I had looked through the paper "DeepFashion: Powering Robust Clothes Recognition and Retrieval
with Rich Annotations" but I could not find any experiments about bounding box detection. So I would like to ask something about training facts of your network.
It seems that you used bounding box annotations (not the landmarks ones) and trained the public fast r-cnn (I mean, a non-customized version) with the boxes. Am I on the right track?
Could you explain some details about training/testing data?
(1) training data type: only in-shop(or consumer) images or both of them
(2) the number of images: training and testing, respectively
Could you share your evaluation methods and results?
By the way, thanks for your sharing the models and the codes! It is really awesome and helpful!
The text was updated successfully, but these errors were encountered:
Hello @liuziwei7,
I had looked through the paper "DeepFashion: Powering Robust Clothes Recognition and Retrieval
with Rich Annotations" but I could not find any experiments about bounding box detection. So I would like to ask something about training facts of your network.
It seems that you used bounding box annotations (not the landmarks ones) and trained the public fast r-cnn (I mean, a non-customized version) with the boxes. Am I on the right track?
Could you explain some details about training/testing data?
(1) training data type: only in-shop(or consumer) images or both of them
(2) the number of images: training and testing, respectively
Could you share your evaluation methods and results?
By the way, thanks for your sharing the models and the codes! It is really awesome and helpful!
The text was updated successfully, but these errors were encountered: