INTRODUCTION

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This is a modification of the OpenAI-CLIP repo of moein-shariatnia(https://github.com/moein-shariatnia/OpenAI-CLIP).

The current training dataset supports flicker-8k or flicker-30k, and the image encoder supports Resnet50 or ViT(vit_base_patch16_384).

Text encoder supports only DistilBert like moein-shariatnia.

ENVIRONTMENT SETTING

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$ virtualenv .venv --python=python3.6
$ source .venv/bin/activate
$ pip install -r requirements.txt

EXECUTTION

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• Pretrain

$ python3 pretrain.py

• Inference image from text query

$ python3 inference_text_to_image.py --query={YOUR QUERY}

• Inference text from image query

$ python3 inference_image_to_text.py --query_text={YOUR .txt FILE} --query_image={YOUR .jpg or .png FILE}

CAUTION

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You must set(or check) some options in config.py before pretrain & inference

ex1) dataset("8k" or "30k"): Train dataset(flicker-8k or flicker-30k)

ex2) model_name("resnet50" or "vit_base_patch16_384"): Type of image encoder

ex3) pretrained(True or False): Decide whether to learn by loading pretrain versions of text encoder(DistilBert) and image encoder(resnet50 or ViT)

ex4) batch_size: Set according to the capacity of the machine