===== Image2text ===== This is an ongoing project to generate text descriptions from a product image. This system should automatically generate product description texts that are somehow human readable. The aim is to generate a mass of content for online marketing. ===== Resources =====
    * https://pythonawesome.com/image-captioning-using-cnn-and-transformers-in-python/ * http://vision.is.tohoku.ac.jp/~kyamagu/papers/yashima2016learning.pdf * https://arxiv.org/abs/2203.15350 * https://arxiv.org/abs/2101.10804 * http://cs231n.stanford.edu/slides/2021/lecture_11.pdf (p. 82 - pure transformer image captioning)
Currently, we have 'short_tease' as part of crawler metadata. For otto, we have 4,833,786 teasers + images. ==== References ==== https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning\\ ===== Ticket Backup ===== The idea is, to get images and train a image to text system to automatically generate product description text that are some how human readable. They don't need to be perfect. The aim is to generate a mass of content for online marketing. Example: I choose TVs and get 1000 TV from OTTO. We get the TV image, some metadata and get a text about the product.