CLIP-GmP-ViT-L-14
Maintainer: zer0int
184
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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