Zer0int

CLIP-GmP-ViT-L-14

zer0int

223

The CLIP-GmP-ViT-L-14 model is a fine-tuned version of OpenAI's CLIP vision-language model that uses Geometric Parametrization (GmP) to achieve unprecedented accuracy on the ImageNet and ObjectNet benchmarks. Developed by zer0int, this model outperforms the original CLIP ViT-L/14 by a significant margin, reaching an accuracy of around 0.90 compared to CLIP's 0.85. Model inputs and outputs Inputs Images**: The model takes in images as input, which it encodes using a Vision Transformer (ViT) architecture. Text**: The model also accepts text inputs, which are encoded using a masked self-attention Transformer. Outputs Image-text similarity**: The primary output of the model is a score representing the similarity between the input image and text. This can be used for tasks like zero-shot image classification, where the model matches an image to the most relevant text label. Capabilities The CLIP-GmP-ViT-L-14 model demonstrates impressive performance on a wide range of computer vision tasks, particularly those that require generalization to new categories. Its accuracy on the challenging ImageNet and ObjectNet benchmarks is a significant improvement over the original CLIP model, showcasing the benefits of the Geometric Parametrization technique. What can I use it for? The CLIP-GmP-ViT-L-14 model could be valuable for a variety of applications that involve matching images to text, such as: Zero-shot image classification**: Classify images into a large number of categories without the need for fine-tuning on labeled data. Image search and retrieval**: Find relevant images based on natural language queries. Visual question answering**: Answer questions about the contents of an image. Things to try One interesting aspect of the CLIP-GmP-ViT-L-14 model is its ability to learn "adverb neurons" - specific neurons that capture adverbial information in the text encoding. This could enable the model to understand and generate more nuanced and expressive language when describing visual content.

Read more