vit-base-patch16-224-in21k

Maintainer: google

Total Score

149

Last updated 5/28/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The vit-base-patch16-224-in21k is a Vision Transformer (ViT) model pre-trained on the large ImageNet-21k dataset, which contains 14 million images and 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 the Google Research vision_transformer repository.

Similar models include the vit-base-patch16-224 model, which was also pre-trained on ImageNet-21k but then fine-tuned on the smaller ImageNet 2012 dataset. The beit-base-patch16-224-pt22k-ft22k model from Microsoft uses a self-supervised pre-training approach on ImageNet-22k before fine-tuning. The CLIP model from OpenAI also uses a Vision Transformer encoder, but is trained with a contrastive loss on web-crawled image-text pairs.

Model inputs and outputs

Inputs

  • Images: The model takes in images as input, which are divided into fixed-size patches (16x16 pixels) and linearly embedded. A special [CLS] token is also added to the sequence.

Outputs

  • Image classification logits: The final output of the model is a vector of logits, corresponding to the predicted probability distribution over the 21,843 ImageNet-21k classes.

Capabilities

The vit-base-patch16-224-in21k model is a powerful image classification model that has been pre-trained on a large and diverse dataset. It can be used for zero-shot classification of images into the 21,843 ImageNet-21k categories. Compared to convolutional neural networks, the Vision Transformer architecture used by this model is better able to capture long-range dependencies in images, which can lead to improved performance on some tasks.

What can I use it for?

You can use the raw vit-base-patch16-224-in21k model for zero-shot image classification on the 21,843 ImageNet-21k classes. For more specialized tasks, you can fine-tune the model on your own dataset - the model hub includes several fine-tuned versions targeting different applications.

Things to try

One interesting aspect of the vit-base-patch16-224-in21k model is its ability to perform well on a wide range of image recognition tasks, even those quite different from the original ImageNet classification problem it was pre-trained on. Researchers have found that the model's internal representations are remarkably general and can be leveraged for tasks like texture recognition, fine-grained classification, and remote sensing. Try experimenting with transferring the model to some of these novel domains to see how it performs.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models

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vit-base-patch16-224

google

Total Score

552

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-patch16-224 and vit-base-patch16-224-in21k models take images as input, which are divided into fixed-size patches and linearly embedded. Sentences/Paragraphs**: The all-mpnet-base-v2 model takes sentences or paragraphs as input and encodes them into a dense vector representation. Low-resolution images and text prompts**: The stable-diffusion-x4-upscaler model takes low-resolution images and text prompts as input, and generates a high-resolution upscaled image. Outputs Image classification logits**: The vit-base-patch16-224 and vit-base-patch16-224-in21k models output logits for each of the 1,000 ImageNet classes. Sentence embeddings**: The all-mpnet-base-v2 model outputs a 768-dimensional vector representation for each input sentence or paragraph. High-resolution upscaled images**: The stable-diffusion-x4-upscaler model 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.

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dino-vitb16

facebook

Total Score

96

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beit-base-patch16-224-pt22k-ft22k

microsoft

Total Score

67

The beit-base-patch16-224-pt22k-ft22k model is a Vision Transformer (ViT) model that was pre-trained in a self-supervised fashion on the large ImageNet-22k dataset, and then fine-tuned on the same dataset. This model was introduced in the paper BEIT: BERT Pre-Training of Image Transformers by researchers from Microsoft. Similar to the original ViT model, the beit-base-patch16-224-pt22k-ft22k model treats images as a sequence of fixed-size patches, which are linearly embedded and then fed into a transformer encoder. However, in contrast to the original ViT, this model uses relative position embeddings instead of absolute position embeddings, and performs classification by mean-pooling the final hidden states of the patches rather than using a [CLS] token. The pre-training objective is also different, using a masked image prediction task inspired by the masked language modeling used in BERT. By pre-training on the large ImageNet-22k dataset, the model learns a rich inner representation of images that can then be used for a variety of downstream computer vision tasks. This beit-base-patch16-224-pt22k-ft22k model can be fine-tuned for tasks like image classification, and may perform better than models trained from scratch on smaller datasets. Model inputs and outputs Inputs Images**: The model takes images as input, which are resized and divided into fixed-size 16x16 patches. These patches are then linearly embedded and fed into the transformer encoder. Outputs Image Features**: The final output of the model is a set of features extracted from the image, which can be used for downstream tasks like image classification. The features are produced by mean-pooling the final hidden states of the patch embeddings. Capabilities The beit-base-patch16-224-pt22k-ft22k model has shown strong performance on image classification tasks, benefiting from the large-scale pre-training on ImageNet-22k. For example, when fine-tuned on the standard ImageNet 2012 dataset, it achieves state-of-the-art results compared to other vision transformer models. What can I use it for? You can use the beit-base-patch16-224-pt22k-ft22k model for a variety of computer vision tasks, especially image classification. The pre-trained features learned by the model can be a great starting point for training classifiers on your own image datasets. To use the model, you can load it from the Hugging Face model hub using the ViTModel class, and then fine-tune it on your own task-specific data. The model hub also has several fine-tuned versions available for different tasks that you can directly use. Things to try One interesting aspect of the beit-base-patch16-224-pt22k-ft22k model is its use of relative position embeddings instead of the more common absolute position embeddings. This allows the model to better capture the spatial relationships between image patches, which can be useful for tasks beyond just classification, such as object detection or segmentation. You could try experimenting with using the representations learned by this model as input features for other computer vision models and tasks, to see how the learned features transfer to different applications. Additionally, you could explore fine-tuning the model on your own specialized image datasets to see how it performs compared to training a model from scratch.

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nsfw_image_detection

Falconsai

Total Score

156

The nsfw_image_detection model is a fine-tuned Vision Transformer (ViT) model developed by Falconsai. It is based on the pre-trained google/vit-base-patch16-224-in21k model, which was pre-trained on the large ImageNet-21k dataset. Falconsai further fine-tuned this model using a proprietary dataset of 80,000 images labeled as "normal" and "nsfw" to specialize it for the task of NSFW (Not Safe for Work) image classification. The fine-tuning process involved careful hyperparameter tuning, including a batch size of 16 and a learning rate of 5e-5, to ensure optimal performance on this specific task. This allows the model to accurately differentiate between safe and explicit visual content, making it a valuable tool for content moderation and safety applications. Similar models like the base-sized vit-base-patch16-224 and vit-base-patch16-224-in21k Vision Transformer models from Google are not specialized for NSFW classification and would likely not perform as well on this task. The beit-base-patch16-224-pt22k-ft22k model from Microsoft, while also a fine-tuned Vision Transformer, is focused on general image classification rather than the specific NSFW use case. Model inputs and outputs Inputs Images**: The model takes images as input, which are resized to 224x224 pixels and normalized before being processed by the Vision Transformer. Outputs Classification**: The model outputs a classification of the input image as either "normal" or "nsfw", indicating whether the image contains explicit or unsafe content. Capabilities The nsfw_image_detection model is highly capable at identifying NSFW images with a high degree of accuracy. This is thanks to the fine-tuning process, which allowed the model to learn the nuanced visual cues that distinguish safe from unsafe content. The model's performance has been optimized for this specific task, making it a reliable tool for content moderation and filtering applications. What can I use it for? The primary intended use of the nsfw_image_detection model is for classifying images as safe or unsafe for work. This can be particularly valuable for content moderation, content filtering, and other applications where it is important to automatically identify and filter out explicit or inappropriate visual content. For example, you could use this model to build a content moderation system for an online platform, automatically scanning user-uploaded images and flagging any that are considered NSFW. This can help maintain a safe and family-friendly environment for your users. Additionally, the model could be integrated into parental control systems, image search engines, or other applications where it is important to protect users from exposure to inappropriate visual content. Things to try One interesting thing to try with the nsfw_image_detection model would be to explore its performance on edge cases or ambiguous images. While the model has been optimized for clear-cut cases of NSFW content, it would be valuable to understand how it handles more nuanced or borderline situations. You could also experiment with using the model as part of a larger content moderation pipeline, combining it with other techniques like text-based detection or user-reported flagging. This could help create a more comprehensive and robust system for identifying and filtering inappropriate content. Additionally, it would be worth investigating how the model's performance might vary across different demographics or cultural contexts. Understanding any potential biases or limitations of the model in these areas could inform its appropriate use and deployment.

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