vit-base-patch16-224
Maintainer: google
552
⚙️
| Property | Value |
|---|---|
| Model Link | View 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 vit-base-patch16-224 is a Vision Transformer (ViT) model pre-trained on ImageNet-21k, a large dataset of 14 million images across 21,843 classes. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. The weights were later converted from the timm repository by Ross Wightman.
The vit-base-patch16-224-in21k model is another ViT model pre-trained on the larger ImageNet-21k dataset, but not fine-tuned on the smaller ImageNet 2012 dataset like the vit-base-patch16-224 model. Both models use a transformer encoder architecture to process images as sequences of fixed-size patches, with the addition of a [CLS] token for classification tasks.
The all-mpnet-base-v2 is a sentence-transformer model that maps sentences and paragraphs to a 768-dimensional dense vector space, enabling tasks like clustering and semantic search. It was fine-tuned on over 1 billion sentence pairs using a self-supervised contrastive learning objective.
The owlvit-base-patch32 model is designed for zero-shot and open-vocabulary object detection, allowing it to detect objects without relying on pre-defined class labels.
The stable-diffusion-x4-upscaler is a text-guided latent diffusion model trained for 1.25M steps on high-resolution images (>2048x2048) from the LAION dataset. It can be used to upscale low-resolution images by 4x while preserving semantic information.
Model inputs and outputs
Inputs
- Images: The
vit-base-patch16-224andvit-base-patch16-224-in21kmodels take images as input, which are divided into fixed-size patches and linearly embedded. - Sentences/Paragraphs: The
all-mpnet-base-v2model takes sentences or paragraphs as input and encodes them into a dense vector representation. - Low-resolution images and text prompts: The
stable-diffusion-x4-upscalermodel takes low-resolution images and text prompts as input, and generates a high-resolution upscaled image.
Outputs
- Image classification logits: The
vit-base-patch16-224andvit-base-patch16-224-in21kmodels output logits for each of the 1,000 ImageNet classes. - Sentence embeddings: The
all-mpnet-base-v2model outputs a 768-dimensional vector representation for each input sentence or paragraph. - High-resolution upscaled images: The
stable-diffusion-x4-upscalermodel generates a high-resolution (4x) upscaled image based on the input low-resolution image and text prompt.
Capabilities
The vit-base-patch16-224 and vit-base-patch16-224-in21k models are capable of classifying images into 1,000 ImageNet classes with high accuracy. The all-mpnet-base-v2 model can be used for a variety of sentence-level tasks, such as information retrieval, clustering, and semantic search. The stable-diffusion-x4-upscaler model can generate high-resolution images from low-resolution inputs while preserving semantic information.
What can I use it for?
The vit-base-patch16-224 and vit-base-patch16-224-in21k models can be used for image classification tasks, such as recognizing objects, scenes, or activities in images. The all-mpnet-base-v2 model can be used to build applications that require semantic understanding of text, such as chatbots, search engines, or recommendation systems. The stable-diffusion-x4-upscaler model can be used to generate high-quality images for use in creative applications, design, or visualization.
Things to try
With the vit-base-patch16-224 and vit-base-patch16-224-in21k models, you can try fine-tuning them on your own image classification datasets to adapt them to your specific needs. The all-mpnet-base-v2 model can be used as a starting point for training your own sentence embedding models, or to generate sentence-level features for downstream tasks. The stable-diffusion-x4-upscaler model can be combined with text-to-image generation models to create high-resolution images from text prompts, opening up new possibilities for creative applications.
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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