the paper Learning Transferable Visual Models From Natural Language Supervision in which they present the CLIP (Contrastive Language–Image Pre-training) model. This model is trained to connect text and images, by matching their corresponding vector representations using a contrastive learning objective. CLIP consists of two separate models, a vision encoder and a text encoder. These were trained on 400 Million images and corresponding captions. We have trained a Farsi (Persian) version of OpenAI's CLIP on a dataset of 400,000 (image, text) pairs. We used
Farahani's RoBERTa-fa as the text encoder and
ViT as the vision encoder from Original CLIP and finetuned them.
- It should be noted that only 400K pairs were used for this training, whereas 4 million pairs were used for the Original CLIP. Also, the training took 30 days across 592 GPUs powered by the V100 chip.
Both models generate vectors with 768 dimensions.
from transformers import CLIPVisionModel, RobertaModel, AutoTokenizer, CLIPFeatureExtractor # download pre-trained models vision_encoder = CLIPVisionModel.from_pretrained('SajjadAyoubi/clip-fa-vision') preprocessor = CLIPFeatureExtractor.from_pretrained('SajjadAyoubi/clip-fa-vision') text_encoder = RobertaModel.from_pretrained('SajjadAyoubi/clip-fa-text') tokenizer = AutoTokenizer.from_pretrained('SajjadAyoubi/clip-fa-text') # define input image and input text text = 'something' image = PIL.Image.open('my_favorite_image.jpg') # compute embeddings text_embedding = text_encoder(**tokenizer(text, return_tensors='pt')).pooler_output image_embedding = vision_encoder(**preprocessor(image, return_tensors='pt')).pooler_output text_embedding.shape == image_embedding.shape
The followings are just some use cases of CLIPfa on 25K
pip install -q git+https://github.com/sajjjadayobi/clipfa.git
from clipfa import CLIPDemo demo = CLIPDemo(vision_encoder, text_encoder, tokenizer) demo.compute_text_embeddings(['گاو' ,'اسب' ,'ماهی']) demo.compute_image_embeddings(test_df.image_path.to_list())
Online Demo: CLIPfa at Huggingface🤗 spaces
We used a small set of images (25K) to keep this app almost real-time, but it's obvious that the quality of image search depends heavily on the size of the image database.
Made with ❤️ in my basement🤫
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