Model Card: CLIP Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here. Model Details The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero shot manner. It was not developed for general model deployment to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within. Model Date January 2021 Model Type The base model uses a ViT L/14 Transformer architecture as an image encoder and uses a masked self attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The original implementation had two variants: one using a ResNet image encoder and the other using a Vision Transformer. This repository has the variant with the Vision Transformer. Documents Blog Post CLIP Paper Use with Transformers Model Use Intended Use The model is intended as a research output for research…
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