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\n  * Contrastive Language-Image Pre-training (CLIP forshort) is a state-of-the-art model introduced by OpenAl in February. \n  * CLIP is a neural network trained on about 400 million (text and image) pairs.\n  * Training uses a contrastive learning approach that aims to unify text and images, allowing tasks like image classification to be done with text-image similarity.\n \n* CLIP Architecture:\n  * Two encoders are jointly trained to predict the correct pairings of abatch of training (image, text) examples.\n    \n    * The text encoder's backbone is a transformer model, and the base size uses 63 millions- parameters,12 layers, and a 512-wide modelcontaining 8 attention heads.\n    * The image encoder, on the other hand, uses both a Vision Transformer (ViT) and a ResNet50 as its backbone, responsible for generating the feature representation of the image.\n   \n* Run Code:\n  * Install:\n    1. !pip install git+https://github.com/PrithivirajDamodaran/ZSIC.git\\\n    2. !pip install streamlit\n\n  * Run app:\n    streamlit run app.py\n\n\n\n\u003cimg width=\"434\" alt=\"Screenshot \" src=\"https://github.com/RATHOD-SHUBHAM/CLIP-Classifier/assets/58945964/6855408f-0504-4a94-8375-58ef9859e9f8\"\u003e\n\n---\n\n# Image Search\n\n## SentenceTransformers\nSentenceTransformers provides models that allow to embed images and text into the same vector space.\u003cbr/\u003e\nThis allows to find similar images as well as to implement image search.\n\n## clip-ViT-B-32\nThis is the Image \u0026 Text model CLIP, which maps text and images to a shared vector space\n\n## Usage\n  1. Git clone Repository.\n  2. cd ImageSearch.\n  3. pip install requirements.txt\n\n## Docker Image\n  * [Image](https://hub.docker.com/repository/docker/gibbo96/text2image/general)\n\n---\n\n\u003cimg width=\"367\" alt=\"sc\" src=\"https://github.com/RATHOD-SHUBHAM/CLIP-Classifier/assets/58945964/32eebda7-49e0-49af-9704-1a2375662d81\"\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frathod-shubham%2Fclip-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frathod-shubham%2Fclip-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frathod-shubham%2Fclip-classifier/lists"}