This program extracts a feature from a cloth image and then applies some given manipulations. The output is the feature of the manipulated cloth image. After that, the image will be retrieved from the dataset.
We have the following image:
You can give an input to our model to remove the hood and the zip up from the image. The output will be:
This is a work based on amazon's ADDE-M. The main difference is the support of multiple manipulations, because the original work only supports a single manipulation. The main advantage of our technique is data augmentation. In the amazon's method the query-target couples of training set are only with distance 1, but in our case the couples can be chosen from every image wich distance is < N
- Download amazon's ADDE-M repo
- Clone our repo in the same folder
- Install requirements with
pip install -r requirements.txt
We used Shopping100k: contact the author of the dataset to get access to the images
After downloading that, you can create random couples using:
python3 f_dataset_gen.py
you can choose the maximum distance N of the couples inside the script
After created dataset (check section above), run:
python3 f_train.py
It is possibile to modify some parameters in parameters.py
To evaluate the model use:
python3 f_eval.py
Here is a comparison between our model wrt amazon's one (ADDE-M) and other state-of-the-art models. We obtained a slightly better performances.
